Data Engineer (ETL, Python, SQL)

Job description

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description Summarized Purpose: We are offering an opportunity for a Mid-Level Data Engineer to design, build, test, tune, and support production data pipelines using PySpark, Python, advanced SQL, AWS data services, secure data handling practices, and AI-assisted data engineering capabilities. Education/Experience: Bachelor's degree or equivalent in Computer Science, Information Technology, Data Engineering, or related field 3-5 years of experience in data engineering, ETL development, SQL, AWS data platforms, or production data pipeline support Major Job Responsibilities: Develop, test, tune, and maintain ETL and data pipelines using PySpark, Python, SQL, and AWS services Support ingestion and transformation of flat files, relational databases, APIs, data warehouses, and enterprise data sources Collaborate with business analysts, data architects, QA, DevOps, and senior engineers to implement source-to-target mappings and data solutions Implement CDC, incremental load design, idempotent pipeline processing, and data reconciliation patterns for reliable data movement Maintain technical documentation, mapping specifications, data catalog updates, runbooks, automated tests, and release support materials Knowledge, Skills, and Abilities: Hands-on experience with PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processing Deep knowledge of Redshift performance tuning including distribution keys, sort keys, compression encoding, Spectrum, materialized views, WLM, vacuum, and analyze Strong knowledge of Athena optimization including partition pruning, file formats, compression, schema evolution, and cost-efficient query design Strong understanding of DynamoDB data modeling, access-pattern-based design, capacity planning, GSIs/LSIs, TTL, Streams, and performance tuning Exposure to secure PHI/PII handling including encryption, access controls, auditability, retention, masking, and de-identification where applicable Strong analytical, troubleshooting, documentation, communication, and cross-functional collaboration skills Must Have Skills: PySpark, Python, advanced SQL, ETL development, and data pipeline implementation experience AWS data services experience including S3, Glue, Lambda, Step Functions, ECS, DynamoDB, Redshift, PostgreSQL, SQL Server, and Athena integration Flat-file ingestion, source-to-target mapping, transformation logic, CDC, incremental loads, idempotent processing, reconciliation, and data quality checks CI/CD, GitHub workflows, automated testing, and release management for data pipelines and database changes Problem-solving, production support, debugging, documentation, and Agile delivery skills Good to Have Skills: Exposure to AI-assisted mapping automation and use of LLMs for data cleaning, data quality checks, transformation logic, or documentation Familiarity with RAG patterns, embeddings, vector databases, semantic search, or AI-enabled data discovery solutions Understanding of healthcare data standards such as HL7, FHIR, CCD, claims data, EMR extracts, clinical trial data, and patient de-identification Familiarity with infrastructure as code such as Terraform or CloudFormation, plus Databricks, Snowflake, streaming, observability, or DevOps practices Working Hours: Philippines: 08:00 PM to 05:00 AM PHT