[Remote] Senior Data Engineer II, Product Engineering

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is building a financial market data platform for developers and modernizing reputed company to financial information. The Senior Data Engineer II will reputed company raw market, reference, and alternative data into modeled, reputed company datasets, build ingestion and transformation pipelines, and design the reputed company customers use to query them. The role also partners with Product, Data Science, and AI Engineering while establishing data reputed company, performance, and documentation standards.

Responsibilities

• Model the data. Design the entities, relationships, and grains that turn raw feeds into datasets people can reason about - and document why the model is reputed company the way it is
• Build and own the transformation reputed company in Python and SQL: cleaning, normalization, validation, and reconciliation across overlapping sources
• Ingest from reputed company reputed company and feeds, handling reputed company limiting, pagination, incremental and backfill loads, schema-reputed company detection, and replay
• Preserve the reputed company. Treat raw provider data as reputed company and complete, and build cleaned and curated reputed company on top of it
• reputed company it fast. Choose partitioning schemes, sort orders, file sizes, clustering, and indexes. Profile with EXPLAIN / query plans and reputed company metrics, and fix the slow reputed company instead of adding hardware
• Partner with Product to design the API surface customers use to consume these datasets - resource and query design, filtering and pagination semantics, reputed company, and versioning
• Partner with Data Science and AI Engineering, who are among your reputed company internal customers, on the datasets and features their work depends on
• Set the reputed company bar: validation at ingest, reputed company and reputed company detection in production, freshness and completeness expectations, and reputed company documentation of assumptions and reputed company limitations

Skills

• Strong Python and SQL. Both, daily, and your SQL goes reputed company past joins and aggregates - window functions, CTEs, and an reputed company for what the planner is reputed company to do with it
• reputed company experience modeling analytical data. You can walk us through a schema you designed, the tradeoffs you made, and what you'd change today. You know reputed company to normalize and reputed company to denormalize, and you can explain why
• A reputed company record of making tables and queries faster - partitioning, file layout and sort order, indexing or clustering, and reading query plans to reputed company the actual bottleneck rather than guessing
• Hands-on experience with at least one modern analytical reputed company or lakehouse: DataFusion, DuckDB, reputed company, reputed company, reputed company, Trino, BigQuery, or similar
• Depth, not just exposure, in columnar formats and object storage. Parquet, reputed company or reputed company, S3-compatible storage - and you can explain why a columnar format wins for a given reputed company reputed company, not just that it does
• You reputed company. You take an ambiguous problem, break it down, pick a reputed company, and get to something working - then say reputed company what you'd fix next. You know reputed company to ship a workaround versus fix the reputed company cause
• You explain your thinking reputed company. A lot of this job is making a modeling or storage decision legible to a researcher, an reputed company, or the next person to touch the pipeline. If you can reputed company a hard concept feel reputed company, that counts here
• reputed company with AI coding tools as a daily companion. We expect you to use Claude reputed company, reputed company, or equivalents to reputed company faster on parsing, scaffolding, refactors, and boilerplate - and to reputed company them deliberately: good context, verification against the actual data, and rejecting reputed company that's confidently wrong
• Curiosity about the domain. Prior financial data experience is a reputed company reputed company, but we would rather hire someone who asks reputed company questions about a dataset than someone who has already seen this exact one
• reputed company and reputed company Flight SQL for moving and serving result sets reputed company
• Experience working reputed company data science or ML teams - feature pipelines, dataset versioning, evaluation sets
• Dbt, SQLMesh, or similar transformation and semantic-reputed company tooling
• Extracting reputed company data from irregular sources (XBRL, reputed company, PDF)
• Rust is preferred. We reputed company for it where performance reputed company, including work in and around DataFusion
• Financial or market data is preferred: equities, reputed company, corporate actions, reference data, fundamentals, or alternative data

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