Work ScheduleStandard (Mon-Fri)Environmental ConditionsOfficeJob DescriptionSummarized 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 field3-5 years of experience in data engineering, ETL development, SQL, AWS data platforms, or production data pipeline supportMajor Job Responsibilities:Develop, test, tune, and maintain ETL and data pipelines using PySpark, Python, SQL, and AWS servicesSupport ingestion and transformation of flat files, relational databases, APIs, data warehouses, and enterprise data sourcesCollaborate with business analysts, data architects, QA, DevOps, and senior engineers to implement source-to-target mappings and data solutionsImplement CDC, incremental load design, idempotent pipeline processing, and data reconciliation patterns for reliable data movementMaintain technical documentation, mapping specifications, data catalog updates, runbooks, automated tests, and release support materialsKnowledge, Skills, and Abilities:Hands-on experience with PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processingDeep knowledge of Redshift performance tuning including distribution keys, sort keys, compression encoding, Spectrum, materialized views, WLM, vacuum, and analyzeStrong knowledge of Athena optimization including partition pruning, file formats, compression, schema evolution, and cost-efficient query designStrong understanding of DynamoDB data modeling, access-pattern-based design, capacity planning, GSIs/LSIs, TTL, Streams, and performance tuningExposure to secure PHI/PII handling including encryption, access controls, auditability, retention, masking, and de-identification where applicableStrong analytical, troubleshooting, documentation, communication, and cross-functional collaboration skillsMust Have Skills:PySpark, Python, advanced SQL, ETL development, and data pipeline implementation experienceAWS data services experience including S3, Glue, Lambda, Step Functions, ECS, DynamoDB, Redshift, PostgreSQL, SQL Server, and Athena integrationFlat-file ingestion, source-to-target mapping, transformation logic, CDC, incremental loads, idempotent processing, reconciliation, and data quality checksCI/CD, GitHub workflows, automated testing, and release management for data pipelines and database changesProblem-solving, production support, debugging, documentation, and Agile delivery skillsGood to Have Skills:Exposure to AI-assisted mapping automation and use of LLMs for data cleaning, data quality checks, transformation logic, or documentationFamiliarity with RAG patterns, embeddings, vector databases, semantic search, or AI-enabled data discovery solutionsUnderstanding of healthcare data standards such as HL7, FHIR, CCD, claims data, EMR extracts, clinical trial data, and patient de-identificationFamiliarity with infrastructure as code such as Terraform or CloudFormation, plus Databricks, Snowflake, streaming, observability, or DevOps practicesWorking Hours:Philippines: 08:00 PM to 05:00 AM PHT