Senior Data Engineer

Polymarket

Senior Data Engineer at Polymarket, focusing on building scalable data platforms for a prediction market. Involves OLAP and OLTP layer ownership with emphasis on analytics and data correctness.

Posted 4/29/2026full-timeNew York City • New York • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftApacheBigQueryDistributed SystemsKafkaPostgresPrometheusPythonRustSQL

About the role

Key responsibilities & impact

Own the OLAP analytics layer. Drive our columnar warehouse environment end-to-end: raw event ingestion → cleaned facts and dimensions → business aggregates → API-serving views. You'll own materialized view design, refresh cadences, dictionary catalog, query planning, and cost optimization.
Partner on the OLTP serving layer. Work closely with the team on high-write serving tables that back our product APIs – partition strategy, indexing, trigger pipelines, autovacuum tuning, and bloat monitoring – with sub-100ms read-path discipline.
Shape streaming and data lake infrastructure. Define Kafka topic schema contracts, evolve the S3 lake layout with modern table formats, and contribute to parity-validation tooling that guards data correctness under migration pressure.
Design data models at scale. Work with event-sourced, append-mostly data with chain-reorg semantics. Design the derivative analytics – PnL, realized/unrealized position tracking, cohort metrics – and formalize ownership boundaries between upstream ingestion and downstream analytics.
Coordinate across teams. Negotiate schema contracts with the warehouse-owning team and downstream consumers including frontend, notifications, and third-party integrators.

Requirements

What you’ll need

5+ years of data engineering on production systems serving real users at scale
Deep knowledge of OLTP/OLAP split architectures: you know when a row store wins, when a column store wins, and when to use both
Columnar warehouse expertise: ClickHouse strongly preferred; Snowflake, BigQuery, Redshift, or Apache Pinot accepted if fundamentals are solid
Data lake experience: Parquet, Iceberg (or Delta/Hudi), compaction strategies, S3 layout discipline
Streaming pipeline experience: Kafka, exactly-once vs. at-least-once reasoning, backpressure, consumer-group patterns, schema evolution
Strong data modeling fundamentals: star/snowflake, SCD patterns, CDC, idempotent event sourcing, dimensional vs. event-log tradeoffs
PostgreSQL at scale: partitioning, index design, autovacuum/bloat remediation, query planning, CDC triggers vs. logical replication
SQL fluency at warehouse scale: window functions, CTEs, dictionary-based enrichment, dialect specifics
Distributed systems reasoning: consistency models, event ordering, replay semantics, write-once vs. mutable state, reorg handling
(Plus) EVM indexing experience: rindexer, subgraphs, or comparable – this shortens ramp considerably
(Plus) Rust: you'll touch indexer and validation tooling codebases; comfortable reading and contributing
(Plus) Domain knowledge in DeFi, prediction markets, or order-book systems
(Plus) Observability and SLO thinking: Prometheus metrics design, dashboard discipline, alert-fatigue avoidance
(Plus) Python for SQL tooling, ad-hoc analysis, and one-off migrations
(Plus) Track record shipping a platform migration or greenfield data stack under a hard deadline

Benefits

Comp & perks

Competitive salary & equity
Unlimited PTO
Full Health, Vision, & Dental coverage
401k match
Hardware setup: new MacBook Pro, big display, & accessories

ATS Keywords

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Hard Skills & Tools

data engineeringOLTP/OLAP architecturescolumnar warehousedata lakestreaming pipelinesdata modelingPostgreSQLSQLdistributed systemsRust

Soft Skills

coordinationnegotiation