Lead Data Engineer

Job description

Work Schedule Other Environmental Conditions Office

Job Description

Summarized Purpose: We are seeking a Lead Data Engineer to own the complete lifecycle of enterprise data pipelines from development to production, including roadmap planning, scalable ETL architecture, AWS data services, secure PHI/PII handling, healthcare data standards, AI-assisted mapping automation, data quality, transformation, catalog standards, and RAG-enabled data solutions.

Education/Experience

• Bachelor's degree or equivalent in Computer Science, Information Technology, Data Engineering, or related field
• 7+ years of experience in data engineering, ETL development, cloud data platforms, healthcare or regulated data environments, and production data pipeline delivery Major Job Responsibilities: Design, develop, deploy, and operate scalable ETL and data pipelines using PySpark, Python, advanced SQL, and AWS data services
• Own data pipeline lifecycle from requirements, mapping, development, testing, deployment, monitoring, production support, release management, and future roadmap planning
• Build ingestion and transformation pipelines for flat files, relational databases, APIs, data warehouses, healthcare data sources, and enterprise data platforms
• Implement mapping automation, preferably using AI, along with LLM-assisted data cleaning, transformation, data quality checks, and RAG use cases
• Implement secure handling of PHI/PII data including encryption, access controls, auditability, retention, masking, de-identification, governance, and operational readiness Knowledge, Skills, and Abilities: Advanced expertise in PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processing
• Strong hands-on experience with AWS services including S3, Glue, Lambda, Step Functions, ECS, DynamoDB, Redshift, RDS/PostgreSQL, and related data services Experience with PostgreSQL, SQL Server, Redshift, flat files, complex source-to-target mappings, HL7, claims data, EMR extracts, and clinical trial data
• Knowledge of data cataloging, metadata management, transformation standards, orchestration, monitoring, data quality, CI/CD, automated testing, and production support practices
• Ability to lead technical design, mentor engineers, guide delivery decisions, troubleshoot complex issues, and communicate with cross-functional teams Must Have Skills: Advanced PySpark, Python, advanced SQL, ETL design, and data pipeline engineering expertise AWS data services experience including S3, Glue, Lambda, Step Functions, ECS, DynamoDB, Redshift, PostgreSQL, and SQL Server integration Secure PHI/PII handling, flat-file ingestion, source-to-target mapping, transformation, data catalog, governance, and healthcare data standards experience CI/CD, GitHub workflows, automated testing, release management for data pipelines and database changes, and dev-to-prod pipeline ownership Good to Have Skills: AI-assisted mapping automation and use of LLMs for data cleaning, data quality checks, transformation logic, documentation, and patient de-identification support Experience with RAG patterns, embeddings, vector databases, semantic search, or AI-enabled data discovery solutions
• Familiarity with infrastructure as code such as Terraform or CloudFormation, plus streaming, Databricks, Snowflake, observability, and DevOps practices Working Hours: India: 05:30 PM to 02:30 AM IST Philippines: 08:00 PM to 05:00 AM PHT