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Group Lead - Data Quality Engineer
Ehpv Fa Em2 Oraclecloud Com CX · CTIPL - Gurgaon, Gurgaon, Haryana, IN; Cargoes Technology - Hyderabad, Hyderabad, Telangana, IN · Deleted · Oracle Recruiting Cloud / Fusion HCM
Job facts
| Field | Value |
|---|---|
| Company | Ehpv Fa Em2 Oraclecloud Com CX |
| Title | Group Lead - Data Quality Engineer |
| Normalized title | - |
| Department / team | - |
| Location | Haryana, IN, United States |
| Work model | - |
| Employment type | Full Time |
| Salary | - |
| Status | deleted |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
| Posted / first seen | 2026-04-29 / 2026-05-31 |
| Changed / last seen | 2026-06-20 / 2026-06-18 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Ehpv Fa Em2 Oraclecloud Com CX. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Oracle Recruiting Cloud / Fusion HCM. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Haryana. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Ehpv Fa Em2 Oraclecloud Com CX |
| Source | 53da7105-3932-4efb-bc7c-e59315084296 |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
Description
Description
KEY ACCOUNTABILITIES Data Quality Policy & Framework Implementation
Define and operationalize enterprise Data Quality policies, procedures, and standards.
Establish standardized data quality dimensions and certification frameworks.
Implement scalable validation frameworks across ingestion, transformation, and serving layers.
Embed “quality-by-design” principles into data product lifecycle.
Data Observability Platform Development
Design and implement end-to-end data observability capabilities including:
Data freshness and SLA monitoring
Volume and distribution anomaly detection
Schema drift and pipeline health monitoring
Data lineage validation and reliability tracking
Develop automated alerting and incident detection mechanisms.
Custom Data Applications (DataApps) Development
Build custom Data Quality and Observability applications using:
Databricks native capabilities
Streamlit / Databricks Apps
Python-based backend services
Develop user interfaces enabling:
Data quality rule configuration
Dataset certification workflows
Quality score visualization
Issue tracking and remediation workflows
Enable self-service quality monitoring for engineering and analytics teams.
Azure & Databricks Platform Integration
Implement data quality checks within Azure-based data pipelines and Databricks workflows.
Integrate monitoring with:
ADLS Gen2
Databricks Lakehouse architecture
Batch and streaming pipelines
Develop reusable frameworks leveraging Spark and Delta Lake.
Optimize performance and scalability of quality validation workloads.
Automation & Engineering Excellence
Integrate DQ checks into CI/CD and deployment pipelines.
Develop metadata-driven quality monitoring solutions.
Implement automated remediation and self-healing workflows where applicable.
Ensure auditability, traceability, and governance compliance.
Metrics, Reporting & Adoption
Define enterprise Data Quality KPIs and reliability SLAs.
Build dashboards tracking platform-wide data trust scores.
Drive adoption of standardized DQ practices across engineering teams.
Support audit and compliance reporting initiatives.
Data Quality Score
Leadership & Collaboration
Act as technical lead for Data Quality and Observability engineering.
Mentor engineers on best practices for data reliability.
Collaborate with Data Engineering, Governance, and Platform Architecture teams.
Contribute to long-term evolution of the enterprise data platform.
QUALIFICATIONS, EXPERIENCE AND SKILLS
Education
Bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or related field.
Experience
8+ years of experience in Data Quality engineering roles within Data Platforms/Data Engineering teams.
Proven experience building custom applications on Databricks or data platforms.
Experience designing enterprise Data Quality or Data Observability solutions.
Hands-on experience developing internal data tools or platform applications.
Technical Skills (Required)
Cloud & Data Platform
Strong expertise in:
Microsoft Azure Databricks Lakehouse platform ADLS Gen2 Distributed data processing using Spark Application & DataApp Development Experience building DataApps using: Streamlit Databricks Apps or notebook-based applications Python backend development Experience designing UI-driven data engineering tools or internal platforms. Data Quality & Observability Experience implementing data validation frameworks. Strong SQL and Python programming skills. Knowledge of anomaly detection, monitoring, and data reliability concepts. Engineering & Integration CI/CD integration for data pipelines. REST API integrations and automation workflows. Metadata-driven architectures and lineage concepts. Core Competencies
Platform-first engineering mindset. Strong problem-solving and analytical thinking. Ability to translate governance requirements into scalable technical solutions. Strong stakeholder collaboration and communication skills. Ownership mindset with ability to lead initiatives end-to-end.
Preferred
Experience with Great Expectations, Deequ, Soda, or similar frameworks. Experience with streaming data validation. Exposure to AI-driven data observability or anomaly detection. Experience building enterprise internal developer platforms.
#LI-AA6
Full job record
| Job ID | fc36049602abc05a7d6a2ae2475758ef8c19ed86 |
| Org ID | 43ec9248-3be3-4dbc-821a-1aca69eba827 |
| Source ID | 53da7105-3932-4efb-bc7c-e59315084296 |
| Board ID | 53da7105-3932-4efb-bc7c-e59315084296 |
| Provider | oracle_hcm |
| Provider Job Key | 24611 |
| Title | Group Lead - Data Quality Engineer |
| Normalized Title | — |
| Status | deleted |
| Active | no |
| Location Text | CTIPL - Gurgaon, Gurgaon, Haryana, IN; Cargoes Technology - Hyderabad, Hyderabad, Telangana, IN |
| Department | — |
| Team | — |
| Employment Type | full_time |
| Workplace Type | — |
| Remote Policy | — |
| Country | United States |
| Region | IN |
| City | Haryana |
| Salary Raw | Description KEY ACCOUNTABILITIES Data Quality Policy & Framework Implementation Define and operationalize enterprise Data Quality policies, procedures, and standards. Establish standardized data quality dimensions and certification frameworks. Implement scalable validation frameworks across ingestion, transformation, and serving layers. Embed “quality-by-design” principles into data product lifecycle. Data Observability Platform Development Design and implement end-to-end data observability capabilities including: Data freshness and SLA monitoring Volume and distribution anomaly detection Schema drift and pipeline health monitoring Data lineage validation and reliability tracking Develop automated alerting and incident detection mechanisms. Custom Data Applications (DataApps) Development Build custom Data Quality and Observability applications using: Databricks native capabilities Streamlit / Databricks Apps Python-based backend services Develop user interfaces enabling: Data quality rule configuration Dataset certification workflows Quality score visualization Issue tracking and remediation workflows Enable self-service quality monitoring for engineering and analytics teams. Azure & Databricks Platform Integration Implement data quality checks within Azure-based data pipelines and Databricks workflows. Integrate monitoring with: ADLS Gen2 Databricks Lakehouse architecture Batch and streaming pipelines Develop reusable frameworks leveraging Spark and Delta Lake. Optimize performance and scalability of quality validation workloads. Automation & Engineering Excellence Integrate DQ checks into CI/CD and deployment pipelines. Develop metadata-driven quality monitoring solutions. Implement automated remediation and self-healing workflows where applicable. Ensure auditability, traceability, and governance compliance. Metrics, Reporting & Adoption Define enterprise Data Quality KPIs and reliability SLAs. Build dashboards tracking platform-wide data trust scores. Drive adoption of standardized DQ practices across engineering teams. Support audit and compliance reporting initiatives. Data Quality Score Leadership & Collaboration Act as technical lead for Data Quality and Observability engineering. Mentor engineers on best practices for data reliability. Collaborate with Data Engineering, Governance, and Platform Architecture teams. Contribute to long-term evolution of the enterprise data platform. QUALIFICATIONS, EXPERIENCE AND SKILLS Education Bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or related field. Experience 8+ years of experience in Data Quality engineering roles within Data Platforms/Data Engineering teams. Proven experience building custom applications on Databricks or data platforms. Experience designing enterprise Data Quality or Data Observability solutions. Hands-on experience developing internal data tools or platform applications. Technical Skills (Required) Cloud & Data Platform Strong expertise in: Microsoft Azure Databricks Lakehouse platform ADLS Gen2 Distributed data processing using Spark Application & DataApp Development Experience building DataApps using: Streamlit Databricks Apps or notebook-based applications Python backend development Experience designing UI-driven data engineering tools or internal platforms. Data Quality & Observability Experience implementing data validation frameworks. Strong SQL and Python programming skills. Knowledge of anomaly detection, monitoring, and data reliability concepts. Engineering & Integration CI/CD integration for data pipelines. REST API integrations and automation workflows. Metadata-driven architectures and lineage concepts. Core Competencies Platform-first engineering mindset. Strong problem-solving and analytical thinking. Ability to translate governance requirements into scalable technical solutions. Strong stakeholder collaboration and communication skills. Ownership mindset with ability to lead initiatives end-to-end. Preferred Experience with Great Expectations, Deequ, Soda, or similar frameworks. Experience with streaming data validation. Exposure to AI-driven data observability or anomaly detection. Experience building enterprise internal developer platforms. #LI-AA6 |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://ehpv.fa.em2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX/job/24611 |
| Apply URL | https://ehpv.fa.em2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX/job/24611 |
| First Seen At | 2026-05-31 18:01:20Z |
| Last Seen At | 2026-06-18 11:20:00Z |
| Last Checked At | 2026-06-20 12:06:39Z |
| Last Changed At | 2026-06-20 12:06:39Z |
| Inactive At | 2026-06-20 12:06:39Z |
| Source Posted At | 2026-04-29 08:10:20Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=oracle_hcm/board=ehpv.fa.em2.oraclecloud.com|CX/date=2026-06-18/2026-06-18T11-18-39-840Z-4008fdb3794d72661360901ac08c6c2426d5440c2e848b1cac03aa6430c3e07b.json |
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"ExternalDescriptionStr": "<figure class=\"table\"><table border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\" style=\"border-collapse: collapse; border-width: medium; border-style: none; border-color: currentcolor; border-image: initial;\"><tbody><tr><td width=\"655\" valign=\"top\" style=\"background-color: rgb(179, 179, 179); border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 491.4pt;\"><h3 style=\"margin: 6pt 0cm;\"><span style=\"color: black; font-family: Tahoma, sans-serif;\"><span lang=\"EN-GB\" style=\"font-size: 10pt;\"><strong>KEY ACCOUNTABILITIES</strong></span></span></h3></td></tr><tr style=\"height: 40pt;\"><td width=\"655\" valign=\"top\" style=\"border: 1pt solid windowtext; height: 40pt; padding: 0cm 5.4pt; width: 491.4pt;\"><ul style=\"padding-left: 24px;\"><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Data Quality Policy & Framework Implementation</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Define and operationalize enterprise Data Quality policies, procedures, and standards.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Establish standardized data quality dimensions and certification frameworks.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Implement scalable validation frameworks across ingestion, transformation, and serving layers.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Embed “quality-by-design” principles into data product lifecycle.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Data Observability Platform Development</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Design and implement end-to-end data observability capabilities including:</span></span></p><ul><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Data freshness and SLA monitoring</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Volume and distribution anomaly detection</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Schema drift and pipeline health monitoring</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Data lineage validation and reliability tracking</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Develop automated alerting and incident detection mechanisms.</span></span></p></li></ul></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Custom Data Applications (DataApps) Development</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Build custom Data Quality and Observability applications using:</span></span></p><ul><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Databricks native capabilities</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Streamlit / Databricks Apps</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Python-based backend services</span></span></p></li></ul></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Develop user interfaces enabling:</span></span></p><ul><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Data quality rule configuration</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Dataset certification workflows</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Quality score visualization</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Issue tracking and remediation workflows</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Enable self-service quality monitoring for engineering and analytics teams.</span></span></p></li></ul></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Azure & Databricks Platform Integration</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Implement data quality checks within Azure-based data pipelines and Databricks workflows.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Integrate monitoring with:</span></span></p><ul><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">ADLS Gen2</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Databricks Lakehouse architecture</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Batch and streaming pipelines</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Develop reusable frameworks leveraging Spark and Delta Lake.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Optimize performance and scalability of quality validation workloads.</span></span></p></li></ul></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Automation & Engineering Excellence</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Integrate DQ checks into CI/CD and deployment pipelines.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Develop metadata-driven quality monitoring solutions.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Implement automated remediation and self-healing workflows where applicable.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Ensure auditability, traceability, and governance compliance.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Metrics, Reporting & Adoption</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Define enterprise Data Quality KPIs and reliability SLAs.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Build dashboards tracking platform-wide data trust scores.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Drive adoption of standardized DQ practices across engineering teams.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Support audit and compliance reporting initiatives.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Data Quality Score</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Leadership & Collaboration</strong></span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Act as technical lead for Data Quality and Observability engineering.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Mentor engineers on best practices for data reliability.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Collaborate with Data Engineering, Governance, and Platform Architecture teams.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Contribute to long-term evolution of the enterprise data platform.</span></span></p></li></ul></td></tr></tbody></table></figure><figure class=\"table\"><table border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\" style=\"border-collapse: collapse; border-width: medium; border-style: none; border-color: currentcolor; border-image: initial;\"><tbody><tr style=\"height: 11.7pt;\"><td width=\"655\" valign=\"top\" style=\"background-color: rgb(179, 179, 179); border: 1pt solid windowtext; height: 11.7pt; padding: 0cm 5.4pt; width: 491.4pt;\"><p class=\"Tablehead\" align=\"left\" style=\"margin: 3pt 0cm; text-align: left;\"><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"EN-GB\" style=\"font-size: 10pt;\"><strong>QUALIFICATIONS, EXPERIENCE AND SKILLS</strong></span></span></p></td></tr><tr style=\"height: 15.75pt;\"><td width=\"655\" valign=\"top\" style=\"border: 1pt solid windowtext; height: 15.75pt; padding: 0cm 5.4pt; width: 491.4pt;\"><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Education</strong></span></span></p><ul style=\"padding-left: 24px;\"><li><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or related field.</span></span></p><p> </p></li></ul><p> </p><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Experience</strong></span></span></p><ul style=\"padding-left: 24px;\"><li><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">8+ years of experience in Data Quality engineering roles within Data Platforms/Data Engineering teams.</span></span></p></li><li><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Proven experience building custom applications on Databricks or data platforms.</span></span></p></li><li><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience designing enterprise Data Quality or Data Observability solutions.</span></span></p></li><li><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Hands-on experience developing internal data tools or platform applications.</span></span></p></li></ul><p> </p><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Technical Skills (Required)</strong></span></span></p><ul style=\"list-style-type: disc; padding-left: 48px;\"><li><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Cloud & Data Platform</span></span></p><p> </p><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Strong expertise in:</strong></span></span></p></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Microsoft Azure</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Databricks Lakehouse platform</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">ADLS Gen2</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Distributed data processing using Spark</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Application & DataApp Development</span></span><ul style=\"list-style-type: disc;\"><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience building DataApps using:</span></span><ul style=\"padding-left: 48px;\"><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Streamlit</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Databricks Apps or notebook-based applications</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Python backend development</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience designing UI-driven data engineering tools or internal platforms.</span></span></li></ul></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Data Quality & Observability</span></span><ul style=\"list-style-type: disc; padding-left: 48px;\"><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience implementing data validation frameworks.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Strong SQL and Python programming skills.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Knowledge of anomaly detection, monitoring, and data reliability concepts.</span></span></li></ul></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Engineering & Integration</span></span><ul style=\"list-style-type: disc; padding-left: 48px;\"><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">CI/CD integration for data pipelines.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">REST API integrations and automation workflows.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Metadata-driven architectures and lineage concepts.</span></span></li></ul></li></ul></li></ul><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Core Competencies</strong></span></span></p><ul style=\"list-style-type: disc; padding-left: 48px;\"><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Platform-first engineering mindset.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Strong problem-solving and analytical thinking.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Ability to translate governance requirements into scalable technical solutions.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Strong stakeholder collaboration and communication skills.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Ownership mindset with ability to lead initiatives end-to-end.</span></span></li></ul><p> </p><p><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\"><strong>Preferred </strong></span></span></p><ul style=\"list-style-type: disc; padding-left: 48px;\"><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience with Great Expectations, Deequ, Soda, or similar frameworks.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience with streaming data validation.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Exposure to AI-driven data observability or anomaly detection.</span></span></li><li><span style=\"font-family: Tahoma, sans-serif;\"><span lang=\"en-AE\" style=\"font-size: 10pt;\">Experience building enterprise internal developer platforms.</span></span></li></ul></td></tr></tbody></table></figure><p> </p><p>#LI-AA6</p>",
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