Data Product Engineer

• Empathetic - we listen, we understand, and are considerate of everyone
• Persevering - we dont give up on challenges or people and go all the way
• Inclusive - we value everyone and their perspectives
• Reliable - we deliver what we promise, and more
• Innovative - we make things better than they were yesterday

Design and deliver the data model, continuous data lineage framework, reconciliation approach, and audit trail infrastructure for governed data platform engagements at regulated financial institutions. Own the semantic and physical models that downstream reporting and reconciliation build against, design and build the lineage framework itself, and set the reconciliation and auditability standard the delivery team builds against.

• Design and document data models for confirmed sources and outputs — source-to-target mappings, transformation requirements, and the treatment approach for data gaps identified during discovery. This includes migration modeling where existing data structures must be reshaped for the target platform.
• Lead data model migration work: map existing source structures to target models, define transformation and enrichment logic, document the migration approach, and validate that migrated data reconciles to source of record.
• Define the semantic model that the reporting layer builds on, in collaboration with the data engineer responsible for the semantic layer and reporting delivery.
• Design and build a continuous data lineage framework — not deploy a lineage product, but build the framework that captures, stores, refreshes, and presents lineage as a first-class capability inside the client environment.
• Integrate lineage generation into the deployment lifecycle so lineage refreshes on every change to the solution, rather than operating as a one-time documentation exercise.
• Define the reconciliation approach across source, transformed, and target datasets. Implement reconciliation checks and design how exceptions surface for investigation.
• Implement the audit trail infrastructure that captures every material data write with actor, timestamp, previous state, new state, and reason code — integrated with the data model so audit works at the semantic level, not just the row level.
• Author test specifications for the data model, lineage, and reconciliation components as part of the build, and integrate automated data quality checks into the deployment pipeline provided by the client.
• Conform to the client enterprise data governance standards for sensitive data, privacy, and access — apply them in the implementation, do not attempt to define them.
• Work as an embedded member of an integrated delivery team: participate in daily standups, sprint planning, and joint reviews with client technical leads, data analysts, and business partners.

• You have led data migration engagements at scale — mapping legacy structures to new platforms, defining transformation logic, and standing behind the reconciliation results. Not just modeling exercises. Real migrations that had to reconcile at go-live.
• You have designed data models for governed data platforms — not just star schemas for a warehouse, but semantic and physical models that downstream analytics, reconciliation, and audit build against.
• You have built continuous data lineage as a framework, not just configured a lineage product. You understand what it takes to capture lineage from ETL code, store it in a graph, refresh it on deployment, and present it in a way business stakeholders can actually read. If your only lineage experience is Collibra or Alation admin, this is not the role.
• You have designed and implemented data reconciliation and audit traceability in a regulated environment. You know what "audit trail" actually means — actor, timestamp, previous state, new state, reason code — and the queries an auditor will ask.
• You have deep AWS data platform expertise: Aurora PostgreSQL, S3, Glue, Athena, Iceberg or comparable table formats, Lambda, and orchestration (Step Functions, MWAA, or comparable). You can design schema, access patterns, and integration points confidently.
• You author test specifications and quality checks as part of building — not as a separate downstream phase. Quality discipline is native to how you work, not an add-on.
• You have worked in a regulated financial services environment and can operate under audit-grade scrutiny.
• You can explain a data model, a lineage graph, and a reconciliation control to a data engineer, a business partner, and a governance SME in the same meeting without losing any of them.
• You are comfortable working inside a client team, participating in their cadence, and delivering ownership outcomes without behaving like a vendor visitor.

• Purposeful Work: autonomy, resources, and time you need to do what matters for our clients and most importantly - you
• Positive Environment: recognition coupled with a flexible environment that celebrates diversity, inclusivity and encourages everyone to achieve their best
• Growth & Development: opportunities to grow professionally and personally through mentorship, training, and development programs
• Transparency & Trust: direction, purpose, mission, and expectations are never a secret; you will always know where you stand as well as where you are going
• Exclusive Perks: discounts on shopping, the gym, insurance, mobile phone plans, not to mention an office smoothie bar and enough coffee to make you vibrate
• Meaningful Benefits: life happens. You will get health, dental, vision, disability, travel insurance, and much more all covered by us
• Unforgettable Events: team bonding, BBQs, office lunches (pizza, always pizza), gala dinners, and any other excuse to have fun