Director, Product Management — Core Database Engine & AI

Our Company

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

What You’ll Do

In this role, you will lead product strategy and execution for Teradata’s core database engine: the massively parallel query engine behind every workload on the platform. You will shape the roadmap across the optimizer, execution runtime, workload management, and Global Planner, and own how the engine performs on native storage, cloud object storage, and open table formats such as Apache Iceberg and Delta Lake.

This role sits at the intersection of the database engine and AI. AI agents are becoming the dominant consumers of enterprise data, issuing high volumes of exploratory, speculative queries, while GenAI and machine learning can improve the engine from the inside. Every core component needs to be rethought in this light, and you will lead that rethink and turn it into a roadmap engineering can build.

Two things anchor the role. Performance: you will spend significant time inside real workloads, query plans, and benchmarks, working with engineering to deliver measurable gains release after release. SQL: you will be the steward of Teradata SQL, the surface customers live in, making its syntax, semantics, and ergonomics simpler, more expressive, and safer for developers, analysts, and AI agents.

• Strategy: Own the engine vision and multi-year roadmap, co-owning architecture direction with senior engineering leaders and making principled trade-offs across performance, cost, and complexity.

• AI and agents: Lead the rethink of every core engine component for AI agents as the new dominant workload, and apply GenAI and ML inside the engine to make it faster and more autonomous.

• Optimizer, execution, and workload management: Agents send many redundant queries. Explore optimizing across queries, sharing intermediate results, and returning early or approximate answers. Apply learned cardinality and cost models and LLM-assisted rewrite, with guardrails that protect plan stability. Define how workload management absorbs bursty machine traffic through agent identity, priorities, and budgets.

• Performance: Work hands-on with engineering on query plans, regressions, and bottlenecks. Own the benchmark suite and KPIs (latency, concurrency, plan stability, price-performance), including agentic workloads, and build regression gates into releases.

• Open Table formats: Own the engine side of Iceberg and Delta Lake (scan planning, pruning, pushdown, write paths) so customers never trade performance for openness. Automate statistics, clustering, and compaction with AI.

• SQL language and query interface: Design features that turn complex problems into simple, readable queries, reduce footguns while keeping power and backward compatibility, and stay aligned with ANSI SQL. Make the dialect something agents can safely generate and execute, and let agents steer with cost estimates, plan explanations, and hints.

Who You’ll Work With

You will work with senior database engineering leaders, performance engineering, AI platform teams, the open table format product team, sales engineering, and executive leadership across the US, Europe, and India. This role starts as an individual contributor, leading through technical depth and influence and mentoring Staff-level PMs. It may grow into managing a team of product managers.

What Makes You a Qualified Candidate

• 15+ years in databases, query engines, or distributed data systems, including senior product management or equivalent principal or staff engineering experience

• Expert understanding of database internals: query planning, cost-based optimization, execution models, and MPP trade-offs

• Deep SQL expertise: a heavy SQL user (data engineer, analyst, or DBA) with strong opinions on language design, ergonomics, and compatibility

• A hands-on performance mindset: reading query plans, designing benchmarks, and finding root causes with senior engineers

• A strong working understanding of LLMs and AI agents, and an informed view on how the…