About The Position

We are looking for a Senior Data Engineer / Data Architect to lead automation-led modernization of legacy data and reporting platforms at scale. This role drives operational automation, repeatable architecture, and AI-assisted tooling, eliminating manual work and accelerating delivery across multiple environments. A core focus is leading the migration of data pipelines and integrations from Informatica to Databricks, while building scalable, repeatable patterns used across states and teams. This is a hands-on, senior role with ownership across architecture, design, and execution.

Requirements

  • 10+ years in data engineering and/or data architecture.
  • Proven track record of modernizing large-scale, complex, legacy data and reporting environments.
  • Hands-on experience migrating or modernizing Informatica-based data pipelines to modern platforms such as Databricks.
  • Demonstrated use of automation frameworks, accelerators, or AI-assisted tools to compress delivery timelines.
  • Hands-on experience with Azure, Cloudera, and Power BI.
  • Strong experience designing and operating ETL/ELT pipelines and analytics layers.
  • Deep understanding of data quality, lineage, reconciliation, and validation frameworks.
  • Experience working in large-scale or regulated enterprise environments.

Nice To Haves

  • Experience in payer, healthcare, or other regulated domains.
  • Experience designing solutions that support multi-environment scalability.
  • Exposure to GenAI tooling for code generation and automation.
  • Experience as a technical thought leader for new platform initiatives.

Responsibilities

  • Assess legacy data and reporting workloads to identify where automation replaces manual effort.
  • Lead the migration of Informatica-based data workloads to Databricks, ensuring performance, reliability, and data integrity
  • Design and execute an automation-first modernization strategy for data pipelines, reporting systems, and analytics platforms.
  • Apply tool-assisted and AI-assisted techniques to accelerate modernization while maintaining compliance and control.
  • Build repeatable frameworks and patterns for: Data ingestion, transformation, and orchestration; Reporting and analytics modernization; Data validation, reconciliation, and quality controls.
  • Establish governance to ensure modernization efforts are consistent, auditable, and scalable.
  • Partner with distributed data teams to deploy and scale modernization patterns across environments.
  • Provide hands-on technical leadership while remaining engaged in execution.
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