Lead Data Engineer

:

• 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