Data Engineer

5 000 – 7 000 $

на 152% выше медианы рынка

≈ 419,9 тыс.–587,8 тыс. ₽

🌍 УдалённоSeniorПолная занятостьАнглийский B2

5 000 – 7 000 $≈ 419,9 тыс.–587,8 тыс. ₽

Хорошие условия

Навык редкий и дорогой (Kubernetes), работа расписана подробно. Но верх вилки на 66% ниже медианы грейда.

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Вакансия на английскомПереведёт заголовок и описание вакансии на русский

Наша компания

The Data Engineering team is responsible for designing, building, and maintaining the Market Data Platform — a lakehouse infrastructure spanning the full path from raw exchange feeds to reliable, petabyte-scale data for research, backtesting, and real-time trading.

О роли

Capture & Ingestion. Own the full capture path from wire to lake: decode and normalize raw exchange feeds (PCAP, multicast UDP, ITCH, FIX) and vendor sources (OneTick, Refinitiv, Bloomberg, ICE) into a unified canonical model with nanosecond timestamps. Build batch and streaming pipelines (Airflow, Spark, dbt) for tick and reference data. Own L2/L3 order-book reconstruction with gap handling. Provide Python and Rust producer SDKs for internal feed handlers

Чем предстоит заниматься

Storage & Modeling — Apache Iceberg. Own the Iceberg-over-S3 lakehouse: design partitioning, sort orders, and row-group layouts for fast scans; manage schema evolution, snapshots, time travel, compaction, and TTL. Maintain reference data as slowly changing tables with point-in-time correctness for backtests. Drive storage cost optimization through compaction, tiering, and snapshot expiry

Tooling & Libraries. Build libraries for schema management, data contracts, validation, and lineage on top of the Iceberg catalog. Develop shared access services (Spark + Polars) so research, backtesting, and trading share a single normalized data layer, including gap detection and PCAP-vs-lake reconciliation

Reliability & Observability. Embed monitoring, alerting, SLAs/SLOs, and CI/CD across capture and pipeline layers on Kubernetes (EKS). Own data-quality dashboards and incident runbooks for the capture fleet

Collaboration. Partner with Quant Research, Data Science, Backend, and DevOps to translate requirements into platform capabilities and champion market-data engineering best practices

Наши требования

5+ years of experience building production-grade data systems, with deep knowledge and understanding of the problems that come with running data lakes/lakehouses at scale: data layout and partitioning strategy, ingestion and backfills, small-file and metadata growth, consistency and late-arriving data, query performance, storage cost, and operational failure modes

Hands-on experience with Apache Iceberg (or comparable table formats such as Delta/Hudi): partitioning, schema evolution, snapshots, compaction, and catalog operations; familiarity with Apache Arrow for zero-copy, columnar in-memory interchange

Expert-level Python (incl. Polars and/or PySpark)

Modern orchestration (Airflow) and distributed processing/query engines (Apache Spark, StarRocks)

Advanced SQL: complex aggregations, window functions, query optimization, partition pruning

Solid fundamentals in Linux, containerization (Docker, Kubernetes/EKS), and cloud object storage (AWS S3)

DevOps & observability: CI/CD, infrastructure-as-code (Terraform), GitOps (ArgoCD), and metrics/dashboards/alerting (Grafana, Prometheus)

Strong grasp of structured + unstructured/binary data and storage optimization: partitioning, compression, cost management

English fluency (B2+) for documentation and collaboration in an international team

Experience with market data and/or network packet capture: decoding PCAP, exchange feed protocols (ITCH, FIX/FAST, multicast UDP), order-book reconstruction, and time-series at scale. Willingness to learn this domain is expected

Experience normalizing market data from multiple vendors (OneTick, Refinitiv/Reuters, Bloomberg, ICE) into a unified schema and symbology

Rust, relevant for high-performance capture/decoding