Data Engineer (AI/ML)

Blue Cross Blue Shield Association•Chicago, IL

2d•$100,800 - $138,600•Hybrid

About The Position

The Data Engineer will design, build, and optimize scalable, secure data pipelines that power analytics and product platforms. For this role specifically, the focus will be on Machine Learning (ML) and Generative Artificial Intelligence (GenAI) workloads, while contributing to innovation and ensuring compliance with healthcare industry standards. This role is expected to provide strong hands-on technical expertise, collaborate across teams, and contribute to architecture decisions that align engineering practices with organizational goals.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
5+ years of experience in data engineering, including building and managing pipelines in cloud-based environments.
Experience with building and operationalizing the data foundations that support machine learning and generative AI use cases, including feature pipelines, training/inference data preparation, and retrieval-ready datasets (e.g., embeddings and vector stores)
Familiarity with GenAI skills and adjacent tooling (foundation models, prompt engineering, RAG, embeddings/vector databases, and GenAI orchestration frameworks).
Hands-on experience with AWS AI/ML and data services, including Amazon Bedrock, Bedrock Agent Core, SageMaker, Glue, and EMR.
Experience designing and optimizing data architectures, including data foundations that support ML and GenAI workloads.
Hands-on experience with workflow orchestration (Airflow) and containerization (Kubernetes).
Hands-on technical expertise, cross-team collaboration, and contributing to architecture decisions
Proficiency in Python, SQL, and distributed data frameworks (PySpark, Databricks, AWS Glue, EMR).
Working knowledge of cloud platforms (AWS or Azure) and data warehouses (Snowflake).
Familiarity with NoSQL and relational databases, as well as data modeling best practices.
Strong analytical, problem-solving, and communication skills.
Understanding of compliance frameworks (SOC 2, HIPAA) and secure data management principles.

Nice To Haves

Experience working with healthcare datasets or knowledge of healthcare standards (HIPAA, HL7, FHIR) preferred.

Responsibilities

Design, build, and maintain reliable, high-performance data pipelines for large-scale structured and unstructured healthcare data.
Use PySpark and modern cloud-based tools (Databricks, AWS Glue, EMR, Snowflake) to transform and process data efficiently.
Support ingestion, transformation, and validation processes that ensure data consistency, integrity, and availability.
Partner with Data Architects, Data Scientists, and Analysts to translate business needs into scalable engineering solutions.
Collaborate with platform and DevOps teams to deploy, scale, and monitor data pipelines using Airflow and Kubernetes.
Participate in code reviews, documentation, and continuous improvement efforts across the engineering team.
Implement and maintain data validation frameworks to ensure pipeline accuracy and completeness.
Contribute to best practices in version control, metadata management, and reproducibility.
Stay current with emerging technologies in data engineering and cloud computing, recommending improvements to existing infrastructure.
Participate in performance tuning, cost optimization, and scaling strategies for cloud-based data systems.
Identify automation opportunities to streamline ETL/ELT processes and reduce operational overhead.
Share knowledge and mentor junior team members on tools, techniques, and best practices.
Promote a culture of collaboration, innovation, and continuous learning within the engineering organization.
Support compliance with SOC 2, HIPAA, and GDPR by adhering to established data privacy and security practices.