Associate Data Engineer, AI Data Products

Lincoln Financial•Radnor, PA

1d•Hybrid

About The Position

Lincoln Financial's AI, Data and Analytics (AIDA) organization is building the capabilities required for an AI-enabled business. Within AIDA, Data Strategy & Governance turns priority insurance and retirement use cases into governed, reusable data products and services. We work backward from the decisions and actions AI agents and applications must support, then build the common meaning, quality, metadata, lineage, and secure access needed to trust the result. We deliver with AIDA's infrastructure, platform, engineering, MLOps, and AI-native platform teams, with governance built into the product. This role will build and operate the data and service components behind Lincoln's priority AI-ready data products. You will use Python and SQL to connect legacy sources, implement transformations and agent-facing interfaces, encode defined business rules, and make testing, lineage, and provenance part of the build. You will learn from senior engineers and business experts while taking increasing ownership of production work.

Requirements

A bachelor's degree or equivalent work experience, plus typically 1 to 3 years of relevant experience or equivalent demonstrated capability.
Hands-on Python and SQL, with a solid grasp of joins, keys, data types, and foundational data modeling.
Working experience with FastAPI, Pydantic, pytest, and testable Python design.
Experience building or extending a Model Context Protocol server or a comparable agent-facing tool interface, plus working knowledge of Git, CI/CD, and a cloud data environment.
Ability to learn an unfamiliar business domain quickly, explain implementation choices clearly, and handle sensitive data with care.

Nice To Haves

AWS Glue, Athena, Lake Formation, IAM, Lambda, ECS, or comparable services.
Dbt, Apache Airflow or Amazon MWAA, Spark, or comparable data-engineering technologies.
Document ingestion, classification, retrieval or RAG data preparation, or experience in insurance, financial services, healthcare, or another regulated industry.

Responsibilities

Build ingestion, transformation, and curated data models using SQL, Python, and approved AWS services.
Build and maintain FastAPI and Pydantic services and Model Context Protocol tools with clear schemas, validation, error handling, and documentation.
Turn source mappings and defined business rules into tested logic; work with business experts to clarify definitions and exceptions.
Write unit, integration, contract, reconciliation, and data-quality tests using pytest, mocks, and reusable fixtures.
Deploy through GitLab CI/CD or comparable pipelines; use logs, metrics, traces, and runbooks to support production workloads.
Capture metadata, lineage, provenance, and access requirements as part of the definition of done.