[Remote] Data Engineer (Dummy)

Note: The job is a remote job and is open to candidates in USA. TALENT Software Services is seeking a Senior Data Engineer to design and operate production ETL/ELT pipelines on enterprise cloud platforms for healthcare data. The role involves end-to-end ownership of pipelines, from requirements through deployment and ongoing operations, while setting technical direction in uncertain situations.

Responsibilities

• Design, build, and operate scalable ETL/ELT pipelines across GCP, Azure, BigQuery, and SQL Server
• Model data for analytical workloads — dimensional modeling, SCDs, and schema design
• Orchestrate pipelines using Airflow, Cloud Composer, Azure Data Factory, or similar
• Handle PHI in line with HIPAA requirements — secure movement, de-identification, access controls, and audit
• Deploy via Git and CI/CD; monitor and maintain pipelines in production
• Translate stakeholder needs into technical plans; communicate feasibility and tradeoffs early

Skills

• 7+ years in data engineering, with significant time at senior level delivering production systems
• Strong SQL plus Java or Python for pipeline development
• Hands-on across multiple cloud stacks (e.g., GCP, Azure, BigQuery, SQL Server)
• Deep experience designing and operating reliable, scalable ETL/ELT pipelines; performance-minded
• Hands-on with at least one orchestration tool (Airflow, Cloud Composer, ADF, Dagster, or Prefect)
• Strong data modeling skills — dimensional modeling, normalization, slowly-changing dimensions
• Working knowledge of HIPAA and PHI handling — secure movement, de-identification, access controls, audit
• Track record delivering on enterprise cloud platforms within standards and controls
• Proficient with Git and CI/CD
• End-to-end ownership: Drives delivery from definition through production with minimal direction; prioritizes operational delivery, not just prototypes
• Business translation: Converts stakeholder needs into clear technical plans; communicates feasibility and value early
• Proactive exploration: Evaluates new tools, runs lightweight proofs of concept, and shares findings
• Industry awareness: Tracks modern data trends (semantic layers, LLM-assisted dev, modern ELT) and brings relevant insights back
• Continuous learning: Adapts quickly while maintaining consistent delivery

Company Overview

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