Product Manager- Data and Analytics Platform

Codvo.ai is seeking an experienced Product Manager to lead the strategy, roadmap, and delivery of data and analytics products for their Oil & Gas and Petrochemical operations. This role involves collaborating with engineering and data science teams to design solutions that enhance production optimization and predictive maintenance while managing the entire product lifecycle using agile methodologies.

Responsibilities:

• Own the product vision, strategy, and roadmap for data and analytics platforms serving Oil & Gas / Petrochemical business units, translating operational and business needs into clear product requirements
• Partner with engineering, data science, and platform teams to design and deliver solutions built on Databricks (Lakehouse architecture, Delta Lake) that support production optimization, predictive maintenance, supply chain analytics, and process safety use cases
• Engage with stakeholders to gather requirements and prioritize features based on business value
• Define and track KPIs and success metrics for product performance, adoption, and business impact
• Manage the end-to-end product lifecycle, from discovery and ideation through launch, adoption, and iteration, using agile methodologies
• Lead cross-functional teams including data engineers, data scientists, UX designers, and QA to deliver high-quality releases on schedule
• Conduct competitive analysis and stay current on emerging trends in industrial data platforms, IoT/OT-IT convergence, and AI/ML applications in energy operations
• Communicate product updates, roadmaps, and business impact to executive leadership and key stakeholders

Requirements:

• 8–10 years of overall experience, including at least 4–5 years in a Product Management role within data, analytics, or enterprise software products
• Demonstrated experience working with Databricks or comparable big data/Lakehouse platforms in a production environment
• Knowledge of the Oil & Gas, Petrochemical, or broader Energy industry, including familiarity with upstream/midstream/downstream operations and relevant industry software (SCADA, PI Historian, ERP/EAM systems, simulation and process engineering tools)
• Proven track record of managing the full product lifecycle for data-intensive or analytics products, from concept to scaled adoption
• Experience working with cross-functional engineering and data science teams in an agile/scrum environment
• Solid understanding of data architecture concepts (data lakes, data warehouses, ETL/ELT pipelines, real-time streaming) and how they apply to industrial use cases
• Strong stakeholder management and communication skills, with the ability to translate technical concepts for both technical and non-technical audiences