London (GB)
Live, first seen by us 70 days agoWork setup in the employer’s posting: hybridFrom the employer’s posting
Employment type Full-time
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
We’re looking for a Data Engineer who can own the journey of data from source systems to decision-ready outputs.
In your first 3 months you’ll have taken ownership of at least one critical pipeline from an external system into Databricks, and built or materially improved a reporting layer used by Fund Management or Asset Management.
Design, build, and maintain scalable data pipelines in Databricks
Own and evolve Databricks data models, schemas and testing
Automate manual data processes with Python, replacing spreadsheets
Build performance dashboards and reports in Streamlit and HTMX
Integrate internal and third-party systems via REST APIs
We’re looking for a Data Engineer who can own the journey of data from source systems to decision-ready outputs. You’ll build and maintain pipelines, model data, stand up dashboards, and integrate systems via APIs. In a team this size, you’ll also get involved in application development - building internal tools with Streamlit, FastAPI, or HTMX that put data directly in the hands of our investment and asset management teams.
In your first 3 months you’ll have taken ownership of at least one critical pipeline from an external system into Databricks, and built or materially improved a reporting layer used by Fund Management or Asset Management.
Design, build, and maintain scalable data pipelines in Databricks
Own and evolve our Databricks models - define schemas, enforce testing, and keep the warehouse clean.
Automate manual data processes using Python, replacing spreadsheets and ad-hoc workflows with robust, repeatable jobs.
Build performance dashboards and reports in Streamlit and HTMX (FastAPI) that Finance, Asset Management, and Fund Management actually use.
Integrate internal and third-party systems via REST APIs, improving data accessibility across the business.
Strong SQL and Python skills, used daily for pipeline development, data modelling, and automation.
a cloud data platform (Databricks preferred)
version control (Git/GitHub)
A solid understanding of data modelling, warehousing patterns, and ETL/ELT design.
To be comfortable working with Postgres or similar relational databases.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 70
Up longer than most. Similar jobs usually come down between day 6 and day 54.
Well past Octoenergy’s usual: its jobs come down by about day 24.
From 11,069 similar postings that came down, and 16 of Octoenergy’s. Coming down is not always a hire.
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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We are embarking on a comprehensive transformation program designed to drive significant advancements in our business operations and technology landscape. Our primary goal is to transition from our current reliance on legacy systems and specialized off-the-shelf tools to a more integrated, AI-driven, and data-centric operational model. Leveraging insights and guidance from our other technically mature entities like Kraken and OE, we plan to adopt cutting-edge tools and capabilities, particularly in AI and Python-driven solutions. By investing in a diverse range of skills, we aim to create a hybrid team capable of operating in both our legacy and future environments, ultimately transforming OEGEN into an efficient, data-driven organization.
- Own and evolve our Databricks models - define schemas, enforce testing, and keep the warehouse clean. - Automate manual data processes using Python, replacing spreadsheets and ad-hoc workflows with robust, repeatable jobs. - Build performance dashboards and reports in Streamlit and HTMX (FastAPI) that Finance, Asset Management, and Fund Management actually use.
What you'll need - Strong SQL and Python skills, used daily for pipeline development, data modelling, and automation. - Experience with modern data stack tooling: a cloud data platform (Databricks preferred), and version control (Git/GitHub).
We’re looking for a Data Engineer who can own the journey of data from source systems to decision-ready outputs. You’ll build and maintain pipelines, model data, stand up dashboards, and integrate systems via APIs. In a team this size, you’ll also get involved in application development - building internal tools with Streamlit, FastAPI, or HTMX that put data directly in the hands of our investment and asset management teams. In your first 3 months you’ll have taken ownership of at least one critical pipeline from an external system into Databricks, and built or materially improved a reporting layer used by Fund Management or Asset Management. What you'll do
What you'll do - Design, build, and maintain scalable data pipelines in Databricks, ensuring data is reliable, timely, and well-documented. - Own and evolve our Databricks models - define schemas, enforce testing, and keep the warehouse clean.
- Design, build, and maintain scalable data pipelines in Databricks, ensuring data is reliable, timely, and well-documented. - Own and evolve our Databricks models - define schemas, enforce testing, and keep the warehouse clean. - Automate manual data processes using Python, replacing spreadsheets and ad-hoc workflows with robust, repeatable jobs.
- Strong SQL and Python skills, used daily for pipeline development, data modelling, and automation. - Experience with modern data stack tooling: a cloud data platform (Databricks preferred), and version control (Git/GitHub). - A solid understanding of data modelling, warehousing patterns, and ETL/ELT design.
About the role We’re looking for a Data Engineer who can own the journey of data from source systems to decision-ready outputs. You’ll build and maintain pipelines, model data, stand up dashboards, and integrate systems via APIs. In a team this size, you’ll also get involved in application development - building internal tools with Streamlit, FastAPI, or HTMX that put data directly in the hands of our investment and asset management teams. In your first 3 months you’ll have taken ownership of at least one critical pipeline from an external system into Databricks, and built or materially improved a reporting layer used by Fund Management or Asset Management.
- Automate manual data processes using Python, replacing spreadsheets and ad-hoc workflows with robust, repeatable jobs. - Build performance dashboards and reports in Streamlit and HTMX (FastAPI) that Finance, Asset Management, and Fund Management actually use. - Integrate internal and third-party systems via REST APIs, improving data accessibility across the business.
Bonus points for - Full-stack development experience - particularly with Streamlit, FastAPI, or HTMX. - Experience building and managing REST APIs, including authentication, rate limiting, and documentation.
- A solid understanding of data modelling, warehousing patterns, and ETL/ELT design. - To be comfortable working with Postgres or similar relational databases. - Clear communication - you can explain a data model to a fund manager and discuss trade-offs with an engineer.
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New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
OEGEN Data, Tech & Transformation Team
We are embarking on a comprehensive transformation program designed to drive significant advancements in our business operations and technology landscape. Our primary goal is to transition from our current reliance on legacy systems and specialized off-the-shelf tools to a more integrated, AI-driven, and data-centric operational model.
Leveraging insights and guidance from our other technically mature entities like Kraken and OE, we plan to adopt cutting-edge tools and capabilities, particularly in AI and Python-driven solutions.
By investing in a diverse range of skills, we aim to create a hybrid team capable of operating in both our legacy and future environments, ultimately transforming OEGEN into an efficient, data-driven organization.
About the role
We’re looking for a Data Engineer who can own the journey of data from source systems to decision-ready outputs. You’ll build and maintain pipelines, model data, stand up dashboards, and integrate systems via APIs. In a team this size, you’ll also get involved in application development - building internal tools with Streamlit, FastAPI, or HTMX that put data directly in the hands of our investment and asset management teams.
In your first 3 months you’ll have taken ownership of at least one critical pipeline from an external system into Databricks, and built or materially improved a reporting layer used by Fund Management or Asset Management.
Full-time
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
London (GB)
Leveraging insights and guidance from our other technically mature entities like Kraken and OE, we plan to adopt cutting-edge tools and capabilities, particularly in AI and Python-driven solutions. By investing in a diverse range of skills, we aim to create a hybrid team capable of operating in both our legacy and future environments, ultimately transforming OEGEN into an efficient, data-driven organization. About the role
No clear work-authorization passage found. Eligibility is unconfirmed.