Sr. Data Engineer, EDM

Medica•Minnetonka, MN

9d•$100,300 - $150,465•Hybrid

About The Position

In this role, you will work with healthcare data ingested into Medica’s Snowflake platform and lead its progression from raw Bronze-layer structures into standardized Silver-layer models aligned with Medica’s Common Information Model (CIM). You will partner with data engineers, architects, analysts, and business subject-matter experts to interpret source data, document business rules, resolve data-quality issues, and make high-value information available across the organization. The ideal candidate combines strong data engineering skills with a practical understanding of healthcare data and business processes. Experience with claims, membership, enrollment, providers, billing, care management, or related health plan subject areas is especially valuable.

Requirements

Bachelor's degree or equivalent experience in related field
7+ years of work experience beyond degree
Significant professional experience in software engineering, data engineering, data integration, data warehousing, or a related discipline.
Advanced SQL skills and experience working with large, complex datasets.
Experience designing and implementing ETL or ELT pipelines.
Experience with dimensional, relational, or enterprise data modeling.

Nice To Haves

Ability to interpret unfamiliar source data and convert it into standardized, business-meaningful models.
Experience implementing data-quality checks, reconciliation processes, automated tests, and production monitoring.
Strong troubleshooting skills across interconnected data pipelines and systems.
Ability to communicate effectively with both technical and nontechnical partners.
Demonstrated ability to work independently, manage ambiguity, and lead complex technical work.
Strong analytical thinking and attention to detail.
Curiosity about how data is created, processed, and used.
Ability to distinguish source-system behavior from enduring business meaning.
Comfort working across technical and business domains.
Pragmatic approach to balancing delivery, maintainability, and data quality.
Strong ownership mindset and commitment to reliable production outcomes.
Experience with Snowflake and cloud-based data platforms.
Experience using dbt or comparable SQL-based transformation frameworks.
Knowledge of medallion architecture, including Bronze, Silver, and Gold data layers.
Experience with Python, orchestration platforms, source control, automated deployment, and CI/CD practices.
Experience working with healthcare payer or health insurance data.
Knowledge of healthcare subject areas such as claims and encounters; membership and enrollment; providers and provider networks; benefits, products, and billing; care management and clinical programs; eligibility and accumulators; or electronic data interchange.
Experience interpreting data from healthcare administration or core processing platforms.
Familiarity with healthcare data governance, privacy, security, and regulatory expectations.
Experience developing canonical, common-information, or enterprise data models.
Experience mentoring engineers or serving as a technical lead while remaining hands-on.

Responsibilities

Design, develop, test, and support scalable data pipelines and transformations within Snowflake.
Standardize raw source data into well-defined Silver-layer data models aligned with Medica’s CIM standards.
Analyze complex source data, relationships, business rules, and processing behavior.
Translate healthcare data knowledge into durable transformation logic, documentation, and data-quality controls.
Develop and maintain reusable data models that support analytics, reporting, operational workflows, and downstream applications.
Profile data and identify quality problems, unexpected patterns, missing relationships, and discrepancies between source systems.
Work with subject-matter experts to validate data meaning, transformation rules, and expected outcomes.
Establish automated reconciliation, validation, observability, and testing practices.
Investigate production issues and resolve defects across ingestion, transformation, and consumption layers.
Contribute to data architecture, modeling, engineering standards, design reviews, and technical decision-making.
Provide technical guidance to other engineers and help improve team engineering practices.
Document data lineage, definitions, transformation logic, dependencies, and operational procedures.
Collaborate across engineering, architecture, analytics, governance, and business teams.