POSITION OVERVIEW

As a Data Engineer, you will design, build, secure, and operate the data pipelines and foundational data products that power enterprise analytics, reporting, applications, and AI capabilities. The team's platform of choice is Microsoft Fabric, and you will build across OneLake, Lakehouse, Warehouse, Data Factory, notebooks, and mirroring, while supporting a disciplined migration off a legacy SQL Server and SSIS environment.

You will work closely with analytics engineers, data solutions analysts, architects, administrators, information security, governance partners, software developers, and business stakeholders. This role calls for strong engineering fundamentals, sound judgment in a regulated environment, and a preference for building reusable solutions over one-off data movement.

RESPONSIBILITIES

• Design, build, test, deploy, and support ingestion and transformation pipelines using Fabric Data Factory, pipelines, notebooks, Dataflows Gen2, mirroring, and shortcuts
• Build and maintain Lakehouse and Warehouse solutions in OneLake using Delta/Parquet, SQL, PySpark, and Spark SQL
• Implement medallion architecture that separates raw ingestion, governed data, and use case specific consumption layers while preserving lineage
• Engineer batch, incremental, change data capture, streaming, and event driven patterns based on source behavior and business need
• Design for schema evolution, historical preservation, replay, and recoverability so pipelines fail safely and resume predictably
• Build reusable frameworks, metadata driven pipelines, and shared libraries that reduce duplicate work
• Migrate and modernize legacy SSIS, SQL Server, and on-premises workloads into Fabric or other approved platforms
• Partner with analytics engineers to publish dependable Silver and Gold assets and governed interfaces for enterprise reporting
• Profile source data, define technical data contracts, and implement automated checks for completeness, accuracy, and freshness
• Apply security by design: least privilege access, workspace permissions, managed identities, and row, column, or object level security where required
• Use source control, peer review, automated testing, and deployment pipelines through GitHub, Azure DevOps, or Fabric Git integration
• Monitor Fabric capacity, pipelines, and Spark workloads, and diagnose failures, performance issues, and cost concerns
• Document architecture, lineage, schemas, and operational procedures for engineering, support, and audit
• Participate in backlog refinement, estimation, incident response, and continuous improvement
• Use approved AI-assisted engineering tools to speed up discovery, coding, testing, and review, while owning correctness and security

QUALIFICATIONS

• Bachelor's degree in computer science, information systems, data analytics, engineering, or a related field, or equivalent experience
• Progressively responsible experience building and supporting production data pipelines, lakehouse or warehouse platforms, or related data infrastructure
• Strong understanding of data architecture, data modeling, orchestration, distributed processing, and cloud data platform concepts
• Ability to troubleshoot complex failures across source systems, networks, identity, storage, orchestration, and compute
• Ability to balance delivery speed with security, governance, reliability, and technical debt
• Working knowledge of Agile delivery practices
• Strong documentation and communication skills, with the ability to explain technical tradeoffs to technical and nontechnical audiences
• Self-directed, collaborative, and comfortable prioritizing multiple initiatives
• Experience in banking, financial services, or another regulated industry is preferred