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Metro location and generic Data Engineer title increase competition, while healthcare specialization slightly narrows the pool.
PHI/PII handling, HL7, and regulated healthcare data expertise create strong industry-specific requirements and reduce transferability.
Explicit 7+ years plus many mandatory technical and regulated-data skills creates highly strict shortlisting filters.
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Own end-to-end lifecycle of scalable ETL and data pipelines including design, development, deployment, monitoring, and production support.
Build and operate data ingestion and transformation pipelines across diverse sources including flat files, relational DBs, APIs, and healthcare-specific data with secure PHI/PII handling.
Plan and execute data platform roadmap, implement AI-assisted mapping automation, data quality frameworks, and lead technical delivery and mentoring of data engineers.
Bachelor's degree or equivalent in Computer Science, IT, Data Engineering, or related field.
7+ years of experience in data engineering covering ETL development, cloud data platforms, healthcare or regulated data, and production pipeline delivery.
Must have advanced skills in PySpark, Python, advanced SQL, AWS data services (S3, Glue, Lambda, Step Functions, ECS, DynamoDB, Redshift, PostgreSQL, SQL Server).
Experience with secure PHI/PII data handling, healthcare data standards, CI/CD, automated testing, and release management for data pipelines.
Experienced in leading technical design and delivery of complex, large-scale healthcare data pipelines with strong ownership of production environments.
Practitioner of advanced AWS cloud data architectures integrating ETL with healthcare data sources and secure data governance.
Familiarity or interest in integrating AI/LLM-based automation for data mapping, quality checks, and advanced data discovery solutions to enhance ETL processes.