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Used Tools & Technologies
Not specified
Required Skills & Competences ?
Security @ 4 Spark @ 7 ETL @ 7 MLOps @ 4 Data Science @ 4 AWS @ 3 AWS EMR @ 3 Communication @ 4 Data Engineering @ 4 ELT @ 7 Product Management @ 6 Hadoop @ 7 Databricks @ 3 Snowflake @ 3 Compliance @ 4 GPU @ 4Details
Our technology has no boundaries! NVIDIA is creating the world’s most groundbreaking and innovative compute platforms for the world to use! It’s because of our work that data engineers and data scientists can advance their ideas. We are looking for individuals to develop a GPU accelerated data processing and ML platform for data scientists to use. In this role, you'll lead and encourage adoption of the data processing service; your work should improve time to first query (TTFQ), QPS metrics, drive platform engagement metrics, and come up with innovative solutions that blend with NVIDIA's groundbreaking enterprise-scale data science platform.
Responsibilities
- Define and Champion the Vision: Own the product vision, strategy, and roadmap for the data platform, ensuring alignment with company objectives and anticipating future data needs.
- Deeply Understand User Needs: Conduct user research and collect requirements from data engineers, data scientists, analysts, and business customers.
- Translate Needs into Actionable Requirements: Convert business and technical requirements into product specifications, user journeys, and acceptance criteria for engineering.
- Prioritize and Manage the Backlog: Balance feature development, technical debt, and platform stability, refining the product backlog.
- Collaborate Cross-Functionally: Work with engineering, design, and data teams throughout the product lifecycle.
- Drive Data Governance and Quality: Promote processes upholding data quality, governance, security, and compliance.
- Go-to-Market Strategy and Adoption: Develop and execute strategies to drive platform adoption including communication, documentation, training, and evangelism.
- Monitor and Iterate: Define KPIs, analyze usage data, and advise future improvements.
- Stay Ahead of the Curve: Monitor trends in data engineering, ML, and AI to find platform evolution opportunities.
Requirements
- 5+ years of product management experience focused on data platforms, infrastructure, or large-scale data products.
- Deep understanding of data engineering technologies such as big data processing (Spark, Hadoop), data warehousing, data lakes (Delta Lake), ETL/ELT pipelines, and streaming data.
- Familiarity with cloud data platforms like Databricks, Snowflake, AWS EMR, Google Dataproc.
- Experience with ML and AI concepts and data platform support for ML lifecycle (feature stores, model training, inference, MLOps).
- Proven ability to define product vision and deliver results.
- Excellent communication and presentation skills, able to articulate complex technical concepts to all audiences.
- Strong analytical and problem-solving skills with data-driven decision-making.
- Bachelor's degree in Computer Science, Engineering, or related technical field; Master's degree preferred.
Ways to Stand Out
- Prior data processing experience at scale on NVIDIA GPUs.
- Experience building or managing products excelling in developer experience (DX).
Benefits
- Eligible for equity and benefits.
- NVIDIA is an equal opportunity employer committed to diversity.