Staff Data Engineer, tem

• 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.):
• Orchestration: Airflow / Dagster
• Warehousing: Snowflake / BigQuery / Redshift / ClickHouse
• Streaming (nice to have): Kafka / Flink
• Experience with cloud platforms (AWS / GCP)
• Experience with data observability and testing practices

Desirable

• 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:
• 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