Full time
Information Technology
Telecommunications
Python
Data Scientist
Testing
Job Description
Requirements
Proven experience operating at staff level (ownership of systems, not just pipelines)
Experience building and scaling modern data platforms
A track record of operating at staff or principal level: you've owned systems, shaped technical direction across teams, and influenced how engineering gets done - not just delivered pipelines
Deep experience building and scaling production data platforms, including high-ingestion time series workloads, and strong hands on ability in Python and modern data stack components (orchestration, warehousing, observability)
The ability to design for reliability and scale - you understand the trade offs in data system design and have made consequential architecture decisions you can speak to clearly
A product mindset: you care about whether the data is actually useful and used, not just whether the pipeline ran green
Experience with cloud data infrastructure (AWS or GCP) and a point of view on what good looks like
The communication skills to lead without authority - influencing technical direction across teams and making the case for the right thing even when it's harder
Strong programming skills in Python, with experience building production grade data systems
Experience with modern data stack components (e.g.):
Experience with data observability and testing practices
(Desirable) Experience in energy or climate tech
(Desirable) Familiarity with time series data at scale
(Desirable) Experience supporting ML pipelines in production
(Desirable) Background in high growth startups or scale ups
What the job involves
We're looking for a Staff Data Engineer to lead the design and evolution of our data platform. This is a high impact, hands on role combining technical leadership, system architecture, and product thinking
You'll work closely with engineering, data science, and energy domain experts to ensure that data is reliable, scalable, and directly drives business value
You'll work across the data management service team alongside data and analytics engineers, and in close partnership with energy domain experts, data scientists, and the broader engineering organisation. This is a hands on senior technical leadership role - you'll be reviewing pull requests and setting architectural direction in the same week
What makes this genuinely different: you're not inheriting someone else's vision of what a data platform should be. The cultural norms, the standards, the practices - these are yours to define. If you've wanted to build the right thing from the ground up, this is that opportunity
Technical Leadership
Shape the technical direction across batch and streaming pipelines, setting the architecture others build to
Set standards for pipeline design and data quality
Lead design reviews and mentor other data engineers
Evaluate and introduce tooling where it raises the bar - and make the case for when it doesn't
Data Platform & Pipelines
Build and maintain robust ETL/ELT pipelines
Build systems optimised for high ingestion, low latency querying of time series data (TSDS)
Optimise pipelines for performance, cost, and reliability
Enable self serve analytics and decision making across the company
Reliability and observability
Implement data quality frameworks with real teeth: SLAs, automated testing, lineage, and monitoring
Establish practices specific to energy data: late arrivals, reprocessing, backfills, and the failure modes that matter in this domain
Build the observability layer that makes the platform trustworthy without constant human oversight
Scale and performance
Identify and fix the bottlenecks that constrain us today
Optimise pipelines for performance, cost, and reliability as data volumes grow
Architect for the next order of magnitude, not just the next quarter
Technical leadership and culture
Set engineering standards for pipeline design, data quality, and system observability
Lead design reviews and mentor data engineers, raising the bar for how the team works
Act as a multiplier: the people around you should get better because of how you approach problems
What Success Looks Like:
What Success Looks Like
The data platform handles tem's current scale without firefighting, and is architected for the next phase of growth
Other teams can access, trust, and use data without routing requests through the data engineering team
There is a tight, reliable feedback loop between data ingestion and consumption: trading, forecasting, and analytics teams make faster decisions because the data is there when they need it
The data engineering team has clearer standards, better practices, and higher output than when you arrived