Data Engineer / Data & AI Engineer

Trulogik•Catonsville, MD

About The Position

We are seeking a hands-on Data Engineer / Data & AI Engineer to build scalable data pipelines and analytical data models that support healthcare payer operations and AI-driven products. The ideal candidate combines strong data engineering, ETL/ELT, and data modeling skills with some exposure to AI/ML and modern data platforms. Experience in health insurance or healthcare payer data is strongly preferred. This role sits at the intersection of data engineering, healthcare data, and applied AI.

Requirements

  • 3+ years of experience in data engineering, ETL/ELT, or data platform development.
  • Strong hands-on experience with Python, SQL, data pipelines, APIs, and relational/data warehouse technologies.
  • Experience with cloud data platforms and tools such as Azure, AWS, Databricks, Snowflake, Redshift, Spark, or similar.
  • Strong understanding of data modeling, schema design, transformation, and data quality.
  • Strong analytical and problem-solving skills, with the ability to work in a fast-moving product environment.

Nice To Haves

  • Exposure to AI/ML, LLMs, feature engineering, or ML pipelines.
  • Experience with health insurance or healthcare payer data, particularly claims, provider, member, pharmacy, or eligibility data.

Responsibilities

  • Design and build scalable data pipelines, ETL/ELT workflows, and data integration frameworks.
  • Develop and maintain analytical and operational data models for claims, provider, member, pharmacy, and related healthcare data.
  • Work with structured and semi-structured data across APIs, databases, files, and cloud platforms.
  • Ensure data quality, lineage, performance, reliability, and observability across pipelines.
  • Support AI/ML use cases by preparing data sets, feature pipelines, inference data flows, and model-ready data.
  • Collaborate with AI, product, and engineering teams to operationalize data-driven solutions.
  • Work with business and domain teams to translate healthcare requirements into scalable data architectures.
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