Senior Data Engineer

Optum

HybridPosted 3 августа 2026 г.

Senior Data Engineer

Optum

HybridPosted 3 августа 2026 г.

About the role

Optum is seeking a Senior Data Engineer to join a team focused on making data usable, trusted, secure, and repeatable. You will handle data discovery, access, pipelines, transformations, quality checks, retrieval layers, metadata, and data-product readiness within an enterprise AI context, where the right data foundations separate scalable solutions from prototypes. The role offers a hybrid schedule and involves collaborating across data engineering, AI, security, privacy, compliance, and governance teams to ensure proper data handling.

Responsibilities

Required qualifications:

Identify required data sources, systems of record, reference data, documents, logs, and operational data.
Assess data availability, quality, completeness, sensitivity, lineage, and access constraints.
Build pipelines, transformations, data products, retrieval indexes, or semantic layers needed by the solution.
Partner with AI engineers on RAG, embeddings, vector stores, grounding strategies, and data refresh patterns.
Create synthetic, masked, de-identified, or test datasets where needed.
Build data quality checks, monitoring, lineage documentation, and exception-handling patterns.
Support control-group testing and value measurement by ensuring baseline and treatment data are reliable.
Prepare data assets and documentation for steady-state ownership.
Bachelor's degree or 5+ years of software engineering experience.
5+ years of data engineering skills across pipelines, SQL, APIs, batch/streaming patterns, and cloud data platforms.
3+ years of experience with dat

About the role

Optum is seeking a Senior Data Engineer to join a team focused on making data usable, trusted, secure, and repeatable. You will handle data discovery, access, pipelines, transformations, quality checks, retrieval layers, metadata, and data-product readiness within an enterprise AI context, where the right data foundations separate scalable solutions from prototypes. The role offers a hybrid schedule and involves collaborating across data engineering, AI, security, privacy, compliance, and governance teams to ensure proper data handling.

Responsibilities

Required qualifications:

Identify required data sources, systems of record, reference data, documents, logs, and operational data.
Assess data availability, quality, completeness, sensitivity, lineage, and access constraints.
Build pipelines, transformations, data products, retrieval indexes, or semantic layers needed by the solution.
Partner with AI engineers on RAG, embeddings, vector stores, grounding strategies, and data refresh patterns.
Create synthetic, masked, de-identified, or test datasets where needed.
Build data quality checks, monitoring, lineage documentation, and exception-handling patterns.
Support control-group testing and value measurement by ensuring baseline and treatment data are reliable.
Prepare data assets and documentation for steady-state ownership.
Bachelor's degree or 5+ years of software engineering experience.
5+ years of data engineering skills across pipelines, SQL, APIs, batch/streaming patterns, and cloud data platforms.
3+ years of experience with dat

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