Data Engineer

**Century Health** is a clinical data intelligence company turning messy real-world clinical data into structured, analysis-ready insights for researchers and pharma teams. --- ## The Role We're looking for a ** Data Engineer (3–5 years)** to be a core builder on our data platform — designing ETL pipelines, profiling raw clinical datasets, and ensuring data flowing through our systems is clean and reliable. --- ## What You'll Do - Design, build, and maintain **ETL/ELT pipelines** ingesting data from CSVs, Parquet, XLSX, APIs, and databases - **Optimize pipeline performance** — tune queries, manage compute, reduce latency and cost - Profile raw datasets to identify quality issues: missing values, duplicates, schema drift, outliers - Build and maintain **dbt models** for clean, documented, analysis-ready data layers - Orchestrate workflows using **Apache Airflow on AWS MWAA** - Process large-scale data using **PySpark on EMR** - Collaborate with ML engineers and GTM teams on downstream use cases - Document pipelines, models, assumptions, and known issues clearly --- ## What We're Looking For **Must-Have** - 3–5 years of professional data engineering experience - Strong **SQL** — window functions, CTEs, query optimization - Solid **Python** — clean, modular, production-grade code - Hands-on **PySpark** for large-scale processing - Experience with **dbt**, **Snowflake**, and cloud-based ETL/ELT - Familiarity with **AWS**: S3, MWAA, ECS Fargate, EMR, RDS, Bedrock - Strong data intuition and ability to work independently in ambiguous environments - Experience with test suites — pytest, Great Expectations, dbt tests - Active use of **AI coding tools** (Cursor, Claude Code, etc.) - Immediate Joiners only (1 month notice period). We can be flexible for exceptional candidates. **Nice to Have** - Healthcare data experience (HIPAA, FHIR, HL7, OMOP) - Data science background (statistical modeling, ML pipelines, feature engineering) - DevOps exposure (CI/CD, Docker, Terraform/CDK) --- ## Tech Stack - **Cloud:** AWS (MWAA, ECS Fargate, EMR, RDS, S3, Bedrock) - **Data Warehouse:** Snowflake - **Transformation:** dbt - **Processing:** PySpark, Python - **Orchestration:** Apache Airflow (MWAA) - **AI Tools:** Cursor, Claude Code --- ## Hiring Process 1. Application Review 2. Take-Home Assignment (~3 hours, real clinical data) 3. Technical Interview 4. Managerial Interview --- ## Why Century Health - Hard data problems with real clinical impact - Small, high-ownership team — your work ships and matters - Modern cloud-native stack with architectural influence - AI-first engineering culture