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At Teradata, we believe that people thrive when empowered with better information. Teradata's 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.

Join the team that built the original Teradata engine, and work on what comes next.

Inside the Office of the CTO, our Advanced Research Team is exploring a new generation of massively parallel, decentralized compute — engine architecture where parallelism is a first principle rather than a later refinement.

That choice shapes everything that follows: the structure of plans, the expression of operators, the organization of storage, and the movement of work and data across nodes. It is the kind of systems problem that can define a career, and we are approaching it with both ambition and rigor.

This is a rare opportunity to work at the frontier of database internals, learn from the people who helped define the field, and leave your mark on a new engine as it takes shape. If you want greenfield systems work backed by the people, resources, and a problem set only a company like Teradata can offer, this is the place.

You will be responsible for building core components of a next-generation parallel compute engine from the ground up:

• Build components across the engine spine: SQL front end (tokenizer, parser, binder), logical/physical plan layer, rule- and cost-based optimizer, and operators (joins, aggregates, sort, scan).


• Build the storage substrate: Arrow in-memory format, slotted-page on-disk format with checksums, buffer pool, and a B-tree or LSM access method.


• Implement transactions and recovery: lock/latch management, MVCC/snapshot isolation, WAL/ARIES, checkpoints, and crash recovery.


• Add parallelism and distribution along the correct axis — exchange-based parallel execution for query work, consensus/replication/atomic-commit for data correctness — without conflating the two.


• Write design specifications before coding (schemas; null/empty/duplicate semantics; memory budget and spill behavior; cost characteristics), write tests first, implement behind the established operator interface, verify safety then speed, and report the benchmark delta.

You will work within an intentionally small Advanced Research Team inside Teradata's Office of the CTO — meaning your contributions directly shape architecture and direction rather than passing through layers of process. You will collaborate with:

• The architects and engineers behind one of the most successful massively parallel databases ever shipped, who have faced the hardest problems in this space and are now focused on what comes next.


• Senior experts who are available to challenge your thinking, share lessons learned from building the first generation, and provide mentorship on deep systems problems.


• Peers who share a commitment to rigor, quality, and first-principles engineering — a small team where everyone's work matters and is visible.

We are open to two complementary profiles for this role:

• Deep expertise in distributed query optimization: cascading optimizers, Abstract Syntax Tree (AST) binding, logical and physical plan distribution.


• Experience designing parallel execution pipelines where distribution is a first-class concern, not an afterthought.


• Familiarity with systems like Apache DataFusion or similar distributed execution frameworks.

• Strong hands-on background with analytical/embedded engines (e.g., DuckDB, DataFusion) and their internals.


• Deep knowledge of pipeline execution, vectorized processing, file system I/O, and open table formats (Iceberg, Delta).


• Experience with transaction management, lock managers, cache management, or OS-level scheduling.

• Systems programming in Rust or modern C/C++ with memory-safety discipline.


• Relational foundations end to end: relational algebra and the algebraic laws behind query rewrites, logical→physical lowering, and cost-based optimization. You can walk the full lifecycle — text → tokens → AST → bound logical plan → optimized plan → physical plan → batches — and reason about cost at every step.


• Demonstrated depth in at least one area of the engine spine (front end, plan/optimizer, operators, storage engine, transactions/recovery, or scale-out) with working literacy across its neighbors.


• Vectorized execution over a columnar (Arrow) representation: batch-at-a-time operators, the pull/iterator model, validity-bitmap-correct null handling, zero-copy buffer sharing.


• Cost reasoning at the metal: cache behavior, alignment, SIMD, allocation patterns, false sharing in parallel accumulators — you can articulate why one physical implementation beats another on a given CPU.


• Test- and benchmark-gated engineering: golden/sqllogictest, property and fuzz testing, deterministic simulation testing for concurrent/distributed paths, and microbenchmarks (Criterion/Google Benchmark) plus a TPC-H subset. Correctness gates before performance, always.


• Sophisticated, hands-on use of AI coding agents directed against a reference-grade spec — managed as a virtual engineering team, held to the same bar as the rest of the work.

• Communicates clearly — can describe a system or problem at the right level of abstraction for any audience, whether a senior architect, a peer, or an AI coding agent.


• Doer over theorist — moves from concept to working prototype quickly; validates by building.


• Intellectually curious and self-directed — digs deep independently before asking for help; brings answers and proposals.


• Collaborative with peers — receptive to input and direction while owning your area end-to-end.


• Comfortable with evolving requirements — writes design documents to create clarity.


• High personal bar for quality.

We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are committed to actively working to foster an inclusive environment that celebrates people for all of who they are.

#LI-CP2

Teradata is proud to be an equal opportunity employer. We do not discriminate based upon race, color, ancestry, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related conditions), national origin, sexual orientation, age, citizenship, marital status, disability, medical condition, genetic information, gender identity or expression, military and veteran status, or any other legally protected status. We welcome and encourage individuals from all backgrounds to apply and join our team, bringing their unique perspectives and experiences to help us innovate and grow. If you require accommodations during the interview process, please let your recruiter know and we will work with you to meet your needs.

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