Product Owner, Data Platform

Product Owner Data Platform JD-0047

Key Responsibilities

Own and manage the Databricks proof-of-concept and data platform product backlog from discovery through delivery and continuous improvement. Define and prioritize data products (e.g. detention analytics ETA accuracy jeopardy intelligence GVP monetization) aligned to business value and customer outcomes. Translate data product strategy into epics user stories and clear acceptance criteria specifically for data and analytics features. Validate product requirements and technical feasibility against Databricks platform capabilities including Delta Lake Unity Catalog and medallion architecture. Partner closely with data architects and data engineering teams to shape data models contracts and platform decisions that impact product capabilities and scalability. Specify data product contracts including schema SLAs data freshness quality thresholds and lineage expectations for all shipped data products. Apply CRISP-DM and expected value framing (e.g. cost of false positives vs. false negatives) to guide ML and analytics feature definition and prioritization. Collaborate with Product Management and Commercial teams on pricing and packaging strategies for data products (per-query per-record subscription tiers) and support scaled Agile/SAFe ceremonies. Required Skills Proven Product Owner experience (812 years) in enterprise B2B SaaS with a strong focus on data and analytics products. Deep understanding of data platforms and data product management including data product contracts (schema SLA freshness lineage). Hands-on fluency in data modeling concepts: star schema slowly changing dimensions (SCD) facts vs. dimensions. Intermediate-plus SQL skills for reading validating and debugging queries (not necessarily writing production-grade SQL). Practical experience with Databricks and core components: Delta Lake Unity Catalog and medallion architecture. Working knowledge of Machine Learning and analytics lifecycles using CRISP-DM or similar frameworks to structure data and ML product development. Ability to define and manage SLAs data freshness requirements and quality thresholds for data products. Experience with pricing and packaging models for data products (e.g. per-query per-record subscription tiers). Strong backlog management skills using Jira including grooming prioritization and release planning. Experience delivering in SAFe or other scaled Agile frameworks collaborating across cross-functional teams. Excellent stakeholder management across data engineering architecture product and customer-facing teams. Strong written communication skills to author precise acceptance criteria for data and analytics features and clear product documentation. Good to Have Skills Experience in logistics transportation supply chain or related domains similar to Intellitrans business context. Familiarity with BI and visualization tools (e.g. Power BI Tableau Looker) for analytics consumption. Understanding of data governance security and compliance best practices in enterprise environments. Background in financial modeling or business case development for data product investments. Certifications in Product Ownership Agile or SAFe. Experience Required 8 to 12 years of experience as a Product Owner or in closely related product roles with a focus on enterprise B2B SaaS data and analytics products

Required Skills:

Proven Product Owner experience (812 years) in enterprise B2B SaaS with a strong focus on data and analytics products. Deep understanding of data platforms and data product management including data product contracts (schema SLA freshness lineage). Hands-on fluency in data modeling concepts: star schema slowly changing dimensions (SCD) facts vs. dimensions. Intermediate-plus SQL skills for reading validating and debugging queries (not necessarily writing production-grade SQL). Practical experience with Databricks and core components: Delta Lake Unity Catalog and medallion architecture. Working knowledge of Machine Learning and analytics lifecycles using CRISP-DM or similar frameworks to structure data and ML product development. Ability to define and manage SLAs data freshness requirements and quality thresholds for data products. Experience with pricing and packaging models for data products (e.g. per-query per-record subscription tiers). Strong backlog management skills using Jira including grooming prioritization and release planning. Experience delivering in SAFe or other scaled Agile frameworks collaborating across cross-functional teams. Excellent stakeholder management across data engineering architecture product and customer-facing teams. Strong written communication skills to author precise acceptance criteria for data and analytics features and clear product documentation.