Senior Data Engineer - Financial Flows
About the Role
As a Senior Data Engineer on the Financial Flows team, you will take full technical ownership of the end-to-end data pipelines, predictive models, and cloud infrastructure powering Kpler’s commodity finance analytics . You will lead the modernization of our data foundation, spearheading the migration of production services to AWS and re-architecting legacy calculation engines into clean, performant Python services . Operating at the crossroads of quantitative research and cloud infrastructure, your work will directly ensure that market participants receive real-time, mission-critical insights across global commodities markets .
Key Responsibilities
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Design, build, and operate resilient batch and real-time streaming data ingestion pipelines connecting to external financial market data providers via APIs and WebSockets .
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Lead the architectural migration of legacy backend infrastructure to modern cloud-native environments on AWS, establishing high standards for system reliability and scalability .
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Drive the refactoring and rewrite of the CTA positioning indicator calculation blocks from PHP to Python, ensuring strict mathematical output parity, comprehensive test coverage, and optimized latency .
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Ingest and model complex financial datasets—including futures pricing, open interest, and trading volumes—across time-series stores and search engines like QuestDB and Elasticsearch .
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Implement automated data validation, proactive alerting, and system monitoring to ensure market data feeds remain accurate, continuous, and fault-tolerant .
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Deploy, maintain, and scale containerized services across Docker and Kubernetes environments using automated CI/CD pipelines and GitOps workflows .
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Partner closely with quantitative researchers, commodity analysts, and product managers to translate complex trading models into high-performance production pipelines .
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Provide technical mentorship to peer engineers, raising team-wide engineering rigor through thoughtful code reviews, system design guidance, and architectural documentation .
Experience & Background
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5+ years of professional experience building, operating, and scaling production-grade data pipelines and backend infrastructure .
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Advanced expertise in Python, with a strong background refactoring and rewriting production-critical code for performance and testability .
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Proven hands-on experience building and maintaining real-time or streaming API ingestion pipelines (REST, WebSockets) handling time-series datasets .
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Solid production experience deploying and managing containerized applications on AWS using Docker, Kubernetes, and modern CI/CD practices .
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Strong database fundamentals in SQL and experience working with distributed search or time-series databases (e.g., Elasticsearch, QuestDB, ClickHouse) .
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Deep experience establishing automated data quality frameworks, observability (monitoring/alerting), and root-cause troubleshooting for production systems .
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Domain knowledge of financial market structures, futures contracts, open interest, and commodities trading symbology .
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Experience integrating data from major financial market vendors (e.g., Bloomberg, LSEG, Enverus, Trading Technologies) .
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Familiarity with legacy PHP codebase migration or reading PHP algorithms to translate into Python .
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Practical familiarity with GitOps frameworks and infrastructure-as-code tooling.
What you'll need (Must-haves)
Nice-to-haves
Source: the employer's careers page. Last checked 2026-10-29. Posted 2026-10-29.
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