Staff Database Internals Engineer

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.

Responsibilities

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, write tests first, implement behind the established operator interface, verify safety then speed, and report the benchmark delta

Requirements

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; 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; 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; Sophisticated, hands-on use of AI coding agents directed against a reference-grade spec; Communicates clearly; Doer over theorist; Intellectually curious and self-directed; Collaborative with peers; Comfortable with evolving requirements; High personal bar for quality; Profile A: Deep expertise in distributed query optimization: cascading optimizers, AST binding, logical and physical plan distribution; Experience designing parallel execution pipelines where distribution is a first-class concern; Familiarity with systems like Apache DataFusion or similar distributed execution frameworks; Profile B: 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

Benefits

People-first culture; Flexible work model; Focus on well-being; Commitment to fostering an inclusive environment