Senior Data Engineer

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    "description": "<p><span style=\"font-weight: bold\">Job Summary</span></p>\n<p>We're opening eyes, hearts and minds to the impact that a pharmacy team can have in changing lives.</p>\n<p> </p>\n<p>Join our group of talented, committed team members-pharmacists, pharmacy care coordinators, technologists, product strategists and more-to create and expand the delivery of personalized health support that people didn't even know could be possible.</p>\n<p><br></p>\n<p>The <span style=\"font-weight: bold\">Senior Data Engineer</span> for Stellus Rx will be a key member of our <span style=\"font-weight: bold\">Technology Team</span>, working closely with Stellus Rx leaders and across the organization to unlock the health of millions of Americans. We are a culture that is unabashedly driven by purpose — making a difference to patients and team members while growing at an accelerated rate.</p>\n<p><br></p>\n<p>This role is built for a data engineer who uses AI as an active part of their workflow — accelerating pipeline development, automating data quality processes, and enabling richer, faster insights across our Cloud Analytics Data Platform rather than relying on manual, repetitive engineering approaches.</p>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Role and Responsibilities:</span></p>\n<p><span style=\"font-weight: bold\">AI-Augmented Pipeline Development &amp; Automation</span></p>\n<ul>\n<li>Develop, construct, and maintain large-scale data processing systems that collect data from a variety of structured and unstructured sources — using AI code generation tools to accelerate pipeline authoring, reduce boilerplate, and improve code quality.</li>\n<li>Build and optimize ELT pipelines using AI-assisted tooling to identify bottlenecks, suggest optimizations, and automate routine pipeline maintenance tasks.</li>\n<li>Identify, design, and implement internal process improvements: use AI to automate manual processes, optimize data delivery, and re-design infrastructure for greater scalability — replacing manual analysis with AI-driven discovery of improvement opportunities.</li>\n<li>Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources; use AI to accelerate infrastructure-as-code authoring and configuration.</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">AI-Ready Data Preparation &amp; ML Enablement</span></p>\n<ul>\n<li>Prepare data for data scientist exploration and discovery using AI-assisted data profiling and quality assessment tools — surfacing anomalies, schema drift, and data gaps faster than manual inspection allows.</li>\n<li>Perform data wrangling and munging for downstream analytics and machine learning; leverage AI tools to generate and validate transformation logic against business rules.</li>\n<li>Assemble large, complex datasets that meet functional and non-functional business requirements; use AI to rapidly evaluate dimensional modeling approaches and ontology alignment strategies.</li>\n<li>Enable large-scale machine learning by designing and maintaining annotated datasets, elastic search approaches, and scalable data lake structures that support AI/ML workloads.</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Analytics Pipeline &amp; Insight Generation</span></p>\n<ul>\n<li>Create and maintain analytics pipelines that generate data and insight to power business decision-making; use AI-assisted analysis to proactively surface trends, anomalies, and opportunities within pipeline outputs.</li>\n<li>Collaborate with data scientists, analysts, and business stakeholders on requirements for dimensional modeling, distributed ETL pipelines, and cross-repository data migration.</li>\n<li>Evaluate, compare, and improve design patterns, data lifecycle approaches, and data ontology alignment — using AI to model trade-offs and accelerate proof-of-concept validation.</li>\n<li>Work with data and analytics experts to continuously improve the functionality, reliability, and intelligence of data systems.</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Root Cause Analysis &amp; Quality Management</span></p>\n<ul>\n<li>Perform root cause analysis on internal and external data and processes using AI-assisted investigation tools — replacing slow, manual log and lineage review with faster, AI-accelerated diagnostics.</li>\n<li>Develop and maintain data quality frameworks; use AI to automate anomaly detection, schema validation, and data contract enforcement across pipelines.</li>\n<li>Develop a strong understanding of company domains, strategic direction, and user needs to ensure data systems are aligned to business outcomes, not just technical requirements.</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Qualifications and Requirements:</span></p>\n<ul>\n<li>4+ years of experience in a Data Engineer role.</li>\n<li>Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.</li>\n<li>Advanced SQL knowledge and experience with relational databases and query authoring.</li>\n<li>Required: Demonstrated, hands-on experience using AI tools to accelerate data engineering tasks — pipeline development, data quality automation, code generation, or root cause analysis — with specific examples you can speak to.</li>\n<li>Experience building and optimizing data pipelines, architectures, and datasets.</li>\n<li>Strong analytic skills working with unstructured and disconnected datasets.</li>\n<li>Experience with big data tools: Hadoop, Spark, Kafka, etc.</li>\n<li>Experience with relational and NoSQL databases including Postgres and Cassandra.</li>\n<li>Experience with pipeline and workflow management tools: Airflow, Luigi, Azkaban, or similar.</li>\n<li>Experience with AWS cloud services: EC2, EMR, RDS, Redshift.</li>\n<li>Experience with stream-processing systems: Storm, Spark Streaming, or similar.</li>\n<li>Working knowledge of message queuing, stream processing, and highly scalable data stores.</li>\n<li>Proficiency in object-oriented/scripting languages: Python, Java, Scala, C++, or similar.</li>\n<li>Experience supporting cross-functional teams in dynamic, agile environments.</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Preferred Experience:</span></p>\n<ul>\n<li>Experience designing or supporting data infrastructure for AI/ML model training, including annotated datasets and feature stores.</li>\n<li>Familiarity with AI-assisted data quality or observability platforms (e.g., Monte Carlo, Soda, or similar).</li>\n<li>Experience with LLM-based data processing pipelines or retrieval-augmented generation (RAG) architectures.</li>\n<li>Healthcare data experience; familiarity with FHIR/HL7 standards a plus.</li>\n<li>High English proficiency<br></li>\n</ul>",
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