Role Overview
We are seeking an experienced Product Owner – Databricks Migration & Data Platform to lead business prioritization, requirements management, backlog ownership, and delivery coordination for a large-scale migration and modernization of enterprise data workloads to Databricks.
The Product Owner will act as the primary bridge between business stakeholders, data owners, Databricks architects, data engineers, source-system SMEs, governance teams, security, application teams, vendors, and program leadership. The role will be responsible for translating business and migration objectives into actionable product capabilities, epics, features, user stories, acceptance criteria, and prioritized delivery roadmaps.
The successful candidate should combine strong Product Ownership / Product Management experience with a solid understanding of enterprise data platforms, Databricks, cloud migration, data governance, data quality, and analytics. The individual does not need to operate as the solution architect but must have sufficient technical depth to effectively challenge requirements, understand architectural dependencies, make prioritization decisions, and drive migration outcomes.
Day-to-Day Job Duties
- Own the product vision, roadmap, backlog, and delivery priorities for Databricks migration and modernization initiatives.
- Partner with Databricks architects and engineering teams to translate the target-state architecture into executable epics, features, user stories, milestones, and release plans.
- Lead discovery workshops with business stakeholders, data owners, application teams, governance teams, and source-system SMEs to understand current-state workloads, pain points, dependencies, and migration requirements.
- Build and maintain the prioritized migration backlog covering:
- Legacy platform/workload migration
- Data ingestion and transformation
- Data-quality remediation
- Governance and metadata
- Security and access
- Reporting and analytics dependencies
- Downstream integrations
- Cutover and decommissioning activities
- Define clear business requirements, functional requirements, user stories, acceptance criteria, Definition of Ready, and Definition of Done.
- Work closely with architecture and engineering teams to assess migration complexity, sequencing, technical dependencies, risks, and business value.
- Prioritize workloads for migration based on business criticality, technical readiness, regulatory requirements, complexity, cost, dependencies, and expected value.
- Drive migration planning from assessment and rationalization through development, testing, reconciliation, cutover, production validation, and legacy decommissioning.
- Coordinate migration waves and release plans across multiple applications, data domains, source systems, and downstream consumers.
- Ensure business requirements are aligned with the Databricks target-state architecture, including Medallion Architecture (Bronze, Silver, Gold), Delta Lake, Unity Catalog, data-quality controls, security, and governed consumption patterns.
- Partner with Data Governance teams to ensure migration requirements incorporate:
- Data ownership and stewardship
- Data classification
- Metadata
- Lineage
- Data quality
- Retention
- Privacy
- Access-control requirements
- Ensure functional and non-functional requirements related to security, performance, scalability, reconciliation, auditability, availability, compliance, and operational support are captured in the backlog.
- Work with architects, engineers, and business SMEs to identify and resolve requirement gaps, cross-team dependencies, data issues, and migration blockers.
- Coordinate source-to-target validation and ensure appropriate acceptance criteria are established for:
- Data completeness
- Data accuracy
- Reconciliation
- Transformation logic
- Historical-data migration
- Performance
- Downstream consumption
- Partner with QA / Quality Engineering teams to define business acceptance criteria and ensure appropriate functional, integration, regression, data-validation, and migration testing.
- Lead backlog refinement, sprint planning, release planning, product demos, stakeholder reviews, and retrospectives in partnership with Scrum Masters and engineering leads.
- Make timely prioritization and scope decisions while balancing business value, regulatory obligations, technical debt, migration risk, and delivery timelines.
- Track migration progress, risks, blockers, dependencies, and decisions and provide clear reporting to business and technology leadership.
- Define and track product and migration KPIs such as:
- Workloads migrated
- Migration velocity
- Data-quality metrics
- Reconciliation success
- Defect leakage
- Platform adoption
- Legacy workloads decommissioned
- Business acceptance
- Cost and performance improvements
- Coordinate User Acceptance Testing and obtain business acceptance / sign-off prior to production migration and cutover.
- Support migration cutover and production-validation activities and help prioritize defects or issues based on business impact.
- Partner with the Databricks Architect to ensure delivery remains aligned with enterprise architecture, governance, security, and engineering standards.
- Collaborate with vendors and system integrators and ensure deliverables align with product priorities, requirements, timelines, and acceptance criteria.
- Communicate product status, roadmap changes, risks, dependencies, and migration outcomes to both technical teams and senior business stakeholders.
Basic Qualifications
- Minimum 8+ years of overall experience in Product Ownership, Product Management, Business Analysis, Data/Analytics delivery, or enterprise technology transformation.
- Minimum 4+ years of experience as a Product Owner / Senior Business Analyst / Product Manager supporting complex enterprise technology or data-platform initiatives.
- Demonstrated experience supporting large-scale data migration, cloud modernization, data-platform transformation, or analytics modernization programs.
- Strong understanding of enterprise data platforms and modern data architectures.
- Working knowledge of Databricks, Lakehouse architecture, Delta Lake, Spark-based processing, and Medallion Architecture (Bronze, Silver, Gold).
- Understanding of Databricks governance concepts, particularly Unity Catalog, data lineage, cataloging, metadata, access governance, data ownership, and data classification.
- Strong understanding of the data lifecycle including:
- Data ingestion
- Transformation
- Storage
- Data quality
- Reconciliation
- Data consumption
- Analytics
- Reporting
- Experience managing requirements and migration backlogs using Jira, Azure DevOps, Rally, or equivalent Agile delivery tools.
- Strong experience writing epics, features, user stories, acceptance criteria, business requirements, and functional requirements.
- Demonstrated ability to prioritize complex backlogs involving multiple business units, technology teams, and interdependent systems.
- Experience with Agile / Scrum / SAFe delivery models, including backlog refinement, sprint planning, PI/release planning, demos, and retrospectives.
- Strong understanding of data migration concepts including:
- Source-to-target mapping
- Data profiling
- Data reconciliation
- Historical-data migration
- Migration waves
- Parallel runs
- Cutover planning
- Production validation
- Legacy decommissioning
- Working knowledge of data governance, data quality, metadata, lineage, privacy, security, regulatory controls, and stewardship.
- Strong stakeholder-management skills with the ability to communicate effectively with business leaders, engineering teams, architects, governance, security, risk, compliance, and executive stakeholders.
- Ability to translate complex technical concepts into clear business outcomes and translate business needs into actionable technical requirements.
- Strong analytical, problem-solving, prioritization, facilitation, and decision-making skills.
- Ability to work onshore in Atlanta with a minimum of 3 days per week in the office.
Travel: As required based on project and client needs; primary work location is Atlanta, Georgia.
Degree: Bachelor's degree in Computer Science, Information Systems, Data Engineering, Business, Engineering, or a related field, or equivalent work experience.
Nice to Have (But Not a Must)
- 10+ years of experience across Product Management, Data & Analytics, Business Analysis, or enterprise transformation programs.
- Experience as a Product Owner specifically for Databricks migrations, lakehouse modernization, or enterprise cloud data-platform programs.
- Experience migrating workloads from legacy platforms such as:
- Hadoop / Hive
- Teradata
- Oracle
- SQL Server
- Informatica
- Traditional data warehouses
- On-premise data lakes
- Experience with enterprise governance/catalog platforms such as Collibra, Alation, Microsoft Purview, or equivalent.
- Experience working with cloud platforms including AWS, Azure, and/or Google Cloud Platform.
- Experience with BI and analytics platforms such as Power BI, Tableau, or equivalent.
- Understanding of APIs, data sharing, semantic layers, machine learning / AI consumption patterns, and downstream analytical applications.
- Experience working in regulated industries such as banking, financial services, insurance, healthcare, or other highly controlled environments.
- Certified Scrum Product Owner (CSPO), SAFe Product Owner/Product Manager (POPM), PMI-PBA, or equivalent Product / Agile certification.
- Databricks fundamentals or relevant Databricks certification.
- Experience working with globally distributed onshore/offshore engineering teams and third-party system integrators.
Leadership & Behavioral Competencies
- Strong product mindset focused on measurable business outcomes rather than simply delivering technical features.
- Excellent stakeholder-management, workshop-facilitation, negotiation, and executive-presentation skills.
- Ability to establish priorities and make decisions in a complex environment with competing business and technology demands.
- Strong understanding of the balance between business value, delivery speed, architecture, governance, risk, security, and technical debt.
- Ability to influence cross-functional teams without direct authority.
- Strong ownership and accountability for delivery outcomes.
- Comfortable operating at both strategic roadmap level and detailed user-story / acceptance-criteria level.
- Strong analytical mindset with the ability to use data and metrics to drive prioritization and decisions.
- Ability to manage ambiguity and convert complex migration challenges into structured, executable plans.
Success Criteria
- Establish a clear and prioritized Databricks migration roadmap and product backlog aligned with business objectives and target-state architecture.
- Successfully migrate prioritized data workloads to Databricks with minimal business disruption.
- Improve alignment between business, architecture, engineering, governance, security, and data stakeholders.
- Establish clear acceptance criteria and measurable controls for data accuracy, completeness, quality, reconciliation, performance, security, and governance.
- Increase migration velocity through effective dependency management, backlog prioritization, and removal of delivery blockers.
- Enable adoption of standardized Bronze, Silver, and Gold data patterns, governed data products, and enterprise Databricks capabilities.
- Ensure migrated workloads comply with enterprise requirements for data governance, metadata, lineage, ownership, access, privacy, and auditability.
- Drive successful business acceptance, production cutovers, and retirement of legacy workloads.
- Provide transparent executive-level reporting of migration progress, business value, risks, dependencies, and outcomes.
- Build a sustainable operating model that enables continued modernization and adoption of the Databricks platform after the initial migration.
Why Join Us
- Opportunity to play a central role in a large-scale Databricks and enterprise data modernization program.
- Work closely with senior architects, engineering teams, data-governance leaders, and business executives.
- Own business outcomes across the complete migration lifecycle—from discovery and prioritization through production cutover and legacy retirement.
- Help shape how enterprise data products are governed, migrated, consumed, and scaled on a modern cloud-native data platform.
- High leadership visibility and strategic impact across business and technology teams.
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