Job Description
The job details for the Data Engineer role have been organized into a more readable format below.
Data Engineer – Health \& Total Rewards (Mercer Compensation AI)
Role Objective
The Data Engineer is the architect of the Data Highway, responsible for designing and building automated ETL/ELT workflows that power Mercer’s consulting tools. The mission is to ingest fragmented data (from insurance carriers, TPAs, and client HRIS) and transform it into a structured format for actuarial modeling and compensation benchmarking, moving millions of sensitive records into a centralized, highperformance analytics environment.
Key Responsibilities
Pipeline Development: Build and maintain robust data pipelines using Python, SQL, and Spark to process largescale healthcare claims and salary survey data.
Data Normalization: Develop logic to clean and standardize diverse data formats.
Data Governance/Compliance: Build automated masking and deidentification routines to ensure HIPAA and GDPR compliance for Protected Health Information (PHI).
Cloud Infrastructure: Deploy and monitor data workloads on Azure (Data Factory/Databricks) or AWS (Glue/Redshift) to ensure high availability and scalability.
Technical Stack \& Requirements
Languages: Expertlevel SQL and Python (specifically for data manipulation via Pandas/PySpark).
Big Data Tools: Handson experience with Databricks, Snowflake, or Hadoop ecosystems.
Orchestration: Experience with Airflow or Azure Data Factory for managing complex job dependencies.
Modeling: Understanding of Star/Snowflake schemas and Data Vault 2\.0 for longterm analytical storage.
Qualifications
Experience: 3–6 years in Data Engineering, ideally within Healthcare, Insurance, or FinTech.
Domain Knowledge: Familiarity with ICD10/CPT codes (medical) or global payroll structures.
Education: Bachelor’s degree in Computer Science, Data Engineering, or a related quantitative field.
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