Product Owner- Databricks- Remote

The Engagement

Client's Data & Analytics practice is embedded in a platform modernization initiative involving three interconnected technology layers:

OMNI a next-generation embedded BI front end replacing Zoho, serving treasury dashboards directly to end-client users inside the application

Databricks a production Lakehouse (Azure-native) powering all analytical pipelines across four active regions (US, Canada, UK, Australia), with isolated instances per region for GDPR compliance

Public Cloud (Azure + AWS) Azure is the primary data movement layer (ADLS, ClickReplicate CDC from SQL Server); AWS hosts Postgres-based Cash Forecasting pipelines feeding into the same Databricks environment

The platform serves real clients in production today and is actively scaling this is not a greenfield build; it is a maturing platform with meaningful architectural and product work ahead

The Opportunity

We are looking for a technically fluent Product Owner who can sit at the intersection of BI delivery, data pipeline operations, and client-facing product outcomes

This is not a strategy-only role you will own the OMNI implementation backlog, coordinate across Databricks engineers, the OMNI vendor team, and GTreasury product stakeholders, and make the decisions that keep delivery moving

You will be the connective tissue between what the data platform can do and what the end user experience in OMNI needs to deliver

What You ll Do

Product Ownership & Backlog Management

Own and maintain the product backlog for the OMNI BI implementation, including semantic layer design, tenant-specific views, caching strategy, and phased rollout across client accounts

Define and document acceptance criteria for OMNI-connected data stories from query behavior at the Databricks Gold layer to what the treasury dashboard renders for an end user

Prioritize backlog items across competing delivery pressures (GDPR compliance per region, April-deadline optimization work, new tenant onboarding)

Run sprint ceremonies, manage dependencies between Databricks engineering work and OMNI vendor deliverables, and surface blockers to engagement lead

Technical Coordination & Platform Alignment

Serve as the informed voice between the OMNI implementation team and the Databricks data engineering team you understand how a Gold-layer view is structured, why caching and trip-wire patterns matter for compute cost, and what tenant isolation via schema-per-tenant means for BI delivery

Partner with data engineers to validate that Gold-layer data models, semantic views, and tenant ID filtering patterns are aligned to what OMNI needs to render correctly

Understand the multi-cloud data movement picture: Azure SQL Server ClickReplicate CDC ADLS Databricks (GT Core); AWS Postgres ADLS Databricks (Cash Forecasting) and identify where handoffs create product risk or latency concerns

Coordinate with the OMNI vendor on caching capabilities, semantic layer configuration, and query behavior (direct query vs. cached sets) to reduce unnecessary Databricks compute load

Stakeholder Engagement & Client Communication

Interface directly with product stakeholders (Senior Product Manager, VP Global Architecture) to align on roadmap priorities, report on delivery status, and escalate architectural decisions that require business input

Translate data engineering constraints (e.g., 30-minute latency SLA, GDPR data residency requirements, cost optimization targets) into product decisions that are legible to non-technical stakeholders

Represent the delivery team in client-facing sessions comfortable owning the room on sprint reviews, backlog grooming with client participants, and status readouts

Governance & Compliance Awareness

Maintain awareness of GDPR data residency requirements across the eight deployed regions no data crossing regional boundaries is a hard constraint, not a preference

Support Unity Catalog governance decisions that affect OMNI access patterns (tenant isolation, row-level security via views, user group policies)

Ensure documentation standards are upheld pipeline behavior, OMNI semantic layer definitions, and acceptance criteria must be maintained in Azure DevOps

What You Bring

Required

5+ years in a Product Owner, Technical Product Manager, or equivalent delivery-focused role on a data or analytics platform

Demonstrated experience owning a BI implementation or analytics product you have been in the room when semantic layer decisions are made and you know how those decisions affect end users

Working knowledge of cloud data platforms you understand what a medallion architecture does, why query patterns against a Gold layer matter for cost and performance, and what CDC-based ingestion looks like end to end

Experience coordinating across multiple vendor and engineering teams simultaneously you know how to unblock delivery without owning every technical decision yourself

Strong backlog management and sprint execution discipline acceptance criteria, definition of done, and dependency tracking are not afterthoughts for you

Comfort in client-facing roles you communicate with precision across both technical and executive audiences

Preferred

Hands-on familiarity with Databricks (Databricks SQL, Unity Catalog, Delta Lake basics) you do not need to write pipelines, but you need to speak the language

Experience with embedded BI tools (OMNI, Looker, Metabase, or equivalent) understanding semantic layers, caching behavior, and direct-query vs. extract tradeoffs

Exposure to multi-tenant SaaS data platforms where tenant isolation, data residency, and per-client SLA management are architectural constraints

Background in fintech, treasury, or financial services data products understanding cash management, payments, or liquidity reporting use cases is a meaningful advantage

Familiarity with GDPR or equivalent data compliance frameworks in a multi-region deployment context

Experience working with Azure DevOps for sprint management and CI/CD coordination

The Mindset We re Looking For

You have been embedded in a fast-moving data platform where the engineering team is smart and the backlog is real and you have made that team faster, not slower

You know that a product decision made without understanding the data model underneath it is a liability, and you have the curiosity to ask the right questions before you commit to a sprint

You are comfortable being the person who connects the OMNI vendor s question about caching to the Databricks engineer s answer about Gold-layer semantics and you do not need the engagement lead to set up that meeting for you

You lead with clarity: a well-written acceptance criterion, a clean dependency map, and a stakeholder status update that does not bury the real risk

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