EIM Data Engineer
The Data Engineer designs and builds the automated ETL/ELT workflows that power Mercer’s consulting tools. You will be responsible for ingesting fragmented data from insurance carriers, third-party administrators (TPAs), and client HRIS systems, transforming it into a structured format for actuarial modeling and compensation benchmarking.Key Responsibilities
Pipeline Development: Build and maintain robust data pipelines using Python, SQL, and Spark to process large-scale healthcare claims and salary survey data.Data Normalization: Develop logic to clean and standardize diverse data formats
Languages: Expert-level SQL and Python (specifically for data manipulation via Pandas/PySpark).Big Data Tools: Hands-on 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 long-term analytical storage.As a Data Engineer for Mercer’s Health & Benefits (H&B) and Compensation domain, you are the architect of the Data Highway. Your mission is to build the pipelines that move millions of sensitive records—from hospital claims to executive payroll—into a centralized, high-performance analytics environment.Job Title: Data Engineer – Health & Total RewardsRole Objective by building automated masking and de-identification 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 & RequirementsLanguages: Expert-level SQL and Python (specifically for data manipulation via Pandas/PySpark).Big Data Tools: Hands-on 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 long-term analytical storage.
QualificationsExperience: 3–6 years in Data Engineering, ideally within Healthcare, Insurance, or FinTech.Domain Knowledge: Familiarity with ICD-10/CPT codes (medical) or global payroll structures.Education: Bachelor’s degree in Computer Science, Data Engineering, or a related quantitative field.