Data Engineering Manager

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    "datePosted": "2026-02-26T05:00:00.000Z",
    "description": "<h2>Overview</h2>\n<h1>Data Engineering Manager</h1>\n<p>We are seeking an experienced Data Engineering Manager in our Hyderabad office to lead a team responsible for designing, building, and operating the scalable data infrastructure and pipelines that power the Care Solutions platform, and for partnering with customers, Engineering, Analytics, Product, and BI teams to ensure reliable, insight-ready data across our business and clients.</p>\n<h2>Areas of Responsibility</h2>\n<h3>Data Engineering Leadership</h3>\n<ul>\n <li>Lead the design, development, and operation of scalable, secure, and high-performance data pipelines and data infrastructure on AWS</li>\n <li>Own the data engineering roadmap, balancing strategic platform investments with near-term delivery priorities</li>\n <li>Architect end-to-end data workflowsincluding ingestion, transformation (ETL/ELT), storage, and delivery, supporting both internal analytics and client-facing product capabilities</li>\n <li>Partner with BI, Analytics, and Data Science teams to model and deliver trusted, well documented datasets</li>\n <li>Establish and enforce data quality, data governance, and data lineage practices across the platform</li>\n <li>Drive adoption of modern data engineering practices including CI/CD for data pipelines, Infrastructure as Code, and observability</li>\n <li>Champion migration and modernization initiatives, including cloud-native data platform evolution on AWS (e.g., Redshift, Glue, Lake Formation, S3)</li>\n <li>Ensure compliance with HIPAA and other healthcare data regulations; implement security best practices for data at rest and in transit</li>\n <li>Proactively identify and remediate data reliability issues, performance bottlenecks, and technical debt</li>\n <li>Champion the use of AI throughout the software development lifecycle from intelligent code generation and automated testing to AI-assisted pipeline monitoring, anomaly detection, and predictive data quality</li>\n</ul>\n<p> </p>\n<h3>Build a High-Performing Team</h3>\n<ul>\n <li>Recruit, mentor, and develop data engineers across data pipeline engineering and data modeling</li>\n <li>Create individualized career growth plans aligned with both team needs and individual aspirations</li>\n <li>Foster a culture of engineering excellence, data ownership, and continuous improvement</li>\n <li>Provide regular coaching and feedback to help engineers grow their technical and leadership capabilities</li>\n <li>Retain and reward high-performing team members</li>\n <li>Promote knowledge sharing, documentation, and internal best practices</li>\n <li>Build effective on-call and incident management practices for production data systems</li>\n <li>Source and hire engineers who embody HealthEdge's core values</li>\n <li>Comfortable leading remote and distributed teams</li>\n</ul>\n<p><strong> </strong></p>\n<h3>Project and Delivery Management</h3>\n<ul>\n <li>Plan, prioritize, and manage project timelines, ensuring on-time delivery of features and integrations</li>\n <li>Break down complex initiatives into manageable tasks and milestones with clear ownership</li>\n <li>Coordinate with product managers to translate business requirements into technical roadmaps</li>\n <li>Manage dependencies and risks across multiple workstreams, escalating proactively when needed</li>\n <li>Establish and track engineering metrics (velocity, quality, uptime) to drive continuous improvement</li>\n <li>Ensure delivery-focused execution while maintaining quality and compliance standards</li>\n <li>Collaborate effectively with US based teams across time zones.</li>\n</ul>\n<p> </p>\n<h2>Required Skills and Experience</h2>\n<ul>\n <li>Degree in Computer Science, Engineering, Statistics, or a related field</li>\n <li>Minimum 12 years of progressive technical experience, including 3+ years managing data engineering teams</li>\n <li>5+ years of hands-on experience as a data engineer, with proven expertise in building production-grade data pipelines</li>\n <li>Deep expertise in AWS data services (e.g., S3, Glue, EMR, Redshift, Athena, Lake Formation, Step Functions)</li>\n</ul>\n<p> </p>\n<p> </p>\n<ul>\n <li>Experience with <strong>MongoDB</strong> including schema design, querying, and integration with data pipelines</li>\n <li>Hands-on experience with ETL/ELT frameworks and workflow orchestration tools (Apache Airflow, AWS Glue, dbt, or similar)</li>\n <li>Experience with data warehousing concepts, dimensional modeling, and data lake/lakehouse architectures</li>\n <li>Familiarity with streaming and batch data processing frameworks (Apache Spark, Kafka, Kinesis, or similar)</li>\n <li>Knowledge of data quality, data observability, and data catalog tooling</li>\n <li>Experience with Infrastructure as Code (Terraform, CloudFormation, or AWS CDK) for data platform components</li>\n <li>Familiarity with CI/CD practices applied to data pipelines and data platform deployments</li>\n <li>Experience with relational databases (MS SQL Server, PostgreSQL) and high-availability configurations</li>\n <li>Proven track record of leading complex data platform migrations or modernization programs</li>\n <li>Strong understanding of data governance, security controls, and compliance frameworks</li>\n</ul>\n<p><strong> </strong></p>\n<h2>Preferred Skills and Experience</h2>\n<ul>\n <li>Healthcare technology experience with deep understanding of HIPAA and data standards (HL7, FHIR)</li>\n <li>Experience with <strong>AWS DynamoDB or AWS DocumentDB</strong> as migration targets or complementary NoSQL solutions</li>\n <li>Hands-on experience with BI and visualization platforms (AWS Quicksight, Tableau, Power BI, or similar)</li>\n</ul>\n<p><strong> </strong></p>\n<h2>Behaviors & Traits</h2>\n<ul>\n <li>Ability to thrive in a fast-paced, dynamic environment with competing priorities</li>\n <li>Excellent communication skills with ability to translate complex data concepts for non-technical stakeholders</li>\n <li>Bias toward automation and eliminating manual, error-prone data processes</li>\n <li>Accepts feedback graciously and creates psychologically safe environments for the team</li>\n</ul>\n<p> </p>\n<p> [NW1]This bullet seems somewhat long/redundant/confusing. Maybe split into 2?</p>\n<p> </p>\n<p> [VS2]Sounds good.</p>",
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