Senior Data Engineer - Financial Flows
United Kingdom
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingAs 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.
Build batch and real-time streaming pipelines from financial data APIs and WebSockets
Lead migration of legacy backend infrastructure to cloud-native AWS
Rewrite CTA positioning indicator calculations from PHP to Python with output parity
Model futures pricing, open interest and volume data in QuestDB and Elasticsearch
Add automated data validation, alerting and monitoring for market data feeds
From the employer’s posting
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.
Design, build, and operate resilient batch and real-time streaming data ingestion pipelines connecting to external financial market data providers via APIs and WebSockets
Lead the architectural migration of legacy backend infrastructure to modern cloud-native environments on AWS
Drive the refactoring and rewrite of the CTA positioning indicator calculation blocks from PHP to Python, ensuring strict mathematical output parity
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
Implement automated data validation, proactive alerting, and system monitoring to ensure market data feeds remain accurate, continuous, and fault-tolerant
Tools in this posting
- PHP
- Python
- SQL
- AWS
- ClickHouse
- Docker
- Elasticsearch
- Kubernetes
Source — Tool mentions in context
- Lead the architectural migration of legacy backend infrastructure to modern cloud-native environments on AWS, establishing high standards for system reliability and scalability. - 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. - 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.
- Experience integrating data from major financial market vendors (e.g., Bloomberg, LSEG, Enverus, Trading Technologies). - Familiarity with legacy PHP codebase migration or reading PHP algorithms to translate into Python. - Practical familiarity with GitOps frameworks and infrastructure-as-code tooling.
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
- 5+ years of professional experience building, operating, and scaling production-grade data pipelines and backend infrastructure. - Advanced expertise in Python, with a strong background refactoring and rewriting production-critical code for performance and testability. - Proven hands-on experience building and maintaining real-time or streaming API ingestion pipelines (REST, WebSockets) handling time-series datasets.
- Solid production experience deploying and managing containerized applications on AWS using Docker, Kubernetes, and modern CI/CD practices. - Strong database fundamentals in SQL and experience working with distributed search or time-series databases (e.g., Elasticsearch, QuestDB, ClickHouse). - Deep experience establishing automated data quality frameworks, observability (monitoring/alerting), and root-cause troubleshooting for production systems.
- Design, build, and operate resilient batch and real-time streaming data ingestion pipelines connecting to external financial market data providers via APIs and WebSockets. - Lead the architectural migration of legacy backend infrastructure to modern cloud-native environments on AWS, establishing high standards for system reliability and scalability. - 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.
- Proven hands-on experience building and maintaining real-time or streaming API ingestion pipelines (REST, WebSockets) handling time-series datasets. - Solid production experience deploying and managing containerized applications on AWS using Docker, Kubernetes, and modern CI/CD practices. - Strong database fundamentals in SQL and experience working with distributed search or time-series databases (e.g., Elasticsearch, QuestDB, ClickHouse).
- Implement automated data validation, proactive alerting, and system monitoring to ensure market data feeds remain accurate, continuous, and fault-tolerant. - Deploy, maintain, and scale containerized services across Docker and Kubernetes environments using automated CI/CD pipelines and GitOps workflows. - Partner closely with quantitative researchers, commodity analysts, and product managers to translate complex trading models into high-performance production pipelines.
- 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. - 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. - Implement automated data validation, proactive alerting, and system monitoring to ensure market data feeds remain accurate, continuous, and fault-tolerant.
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 29, 2026 · 2 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
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
-
Design, build, and operate resilient batch and real-time streaming data ingestion pipelines connecting to external financial market data providers via APIs and WebSockets.
-
Lead the architectural migration of legacy backend infrastructure to modern cloud-native environments on AWS, establishing high standards for system reliability and scalability.
-
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.
-
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.
-
Implement automated data validation, proactive alerting, and system monitoring to ensure market data feeds remain accurate, continuous, and fault-tolerant.
-
Deploy, maintain, and scale containerized services across Docker and Kubernetes environments using automated CI/CD pipelines and GitOps workflows.
-
Partner closely with quantitative researchers, commodity analysts, and product managers to translate complex trading models into high-performance production pipelines.
-
Provide technical mentorship to peer engineers, raising team-wide engineering rigor through thoughtful code reviews, system design guidance, and architectural documentation.
Experience & Background
-
5+ years of professional experience building, operating, and scaling production-grade data pipelines and backend infrastructure.
-
Advanced expertise in Python, with a strong background refactoring and rewriting production-critical code for performance and testability.
-
Proven hands-on experience building and maintaining real-time or streaming API ingestion pipelines (REST, WebSockets) handling time-series datasets.
-
Solid production experience deploying and managing containerized applications on AWS using Docker, Kubernetes, and modern CI/CD practices.
-
Strong database fundamentals in SQL and experience working with distributed search or time-series databases (e.g., Elasticsearch, QuestDB, ClickHouse).
-
Deep experience establishing automated data quality frameworks, observability (monitoring/alerting), and root-cause troubleshooting for production systems.
-
Domain knowledge of financial market structures, futures contracts, open interest, and commodities trading symbology.
-
Experience integrating data from major financial market vendors (e.g., Bloomberg, LSEG, Enverus, Trading Technologies).
-
Familiarity with legacy PHP codebase migration or reading PHP algorithms to translate into Python.
-
Practical familiarity with GitOps frameworks and infrastructure-as-code tooling.
What you'll need (Must-haves)
Nice-to-haves
Employment type
Full-time
Source details
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- Pay
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- Location & working pattern
United Kingdom
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