Senior Data Engineer, tem

• Strong Python skills, with proven experience building and maintaining production data pipelines
• Experience owning the design, reliability, and resilience of a data pipeline or system end-to-end, not just writing pipeline code
• AWS or GCP experience
• Great communication skills, comfortable working directly with stakeholders to scope and agree SLAs, and explaining technical trade-offs to non-technical audiences
• Strong team player, comfortable working independently against a backlog and making judgement calls on prioritisation, while fitting into an existing team of 4
• Terraform experience
• We're open to someone who doesn't tick every box here, as long as they bring solid AWS or GCP experience and the ability to own their own work end-to-end

Desirable

• Data sits at the core of what tem does. Our time-series data is the spine that runs from pricing through to billing: every transaction depends on it working. You'll be working across a genuinely varied set of workloads: textual data, time-series data, and event streams, each with its own ingestion and reliability challenges, including TSDS
• You'll join our Data Service team of 4, owning the design, reliability, and recovery strategy of specific ingestion pipelines and systems end-to-end. This is an individual contributor role: no team to manage, full ownership of the work you do
• The team has more work than hands right now, and this role exists to close that gap so the business can move faster on the data it depends on
• You'll also work closely with our AI initiatives as they grow: the platform you own increasingly becomes the foundation those efforts depend on
• Own the design, reliability, and recovery strategy for the ingestion pipelines and systems in your domain
• Maintain and improve the data platform, including working through the existing tech debt backlog
• Build monitoring and alerting so issues surface before they become incidents
• Set the data quality checks that make an SLA commitment possible, and hold the line on them
• Help take the number of data products with a defined SLA from 2 to 10
• Own the design, reliability, and resilience of ingestion pipelines end-to-end, including TSDS, with a clear recovery strategy when things break
• Build and maintain ETL/ELT pipelines for new and existing data sources
• Decide the right approach (batch vs. streaming) for each pipeline in your domain, including high-scale, low-latency requirements for time-series data
• Set data quality checks that make an SLA commitment possible, and hold the line on them
• Own the day-to-day reliability of the data platform: monitoring, testing, and fixing issues before they become firefights
• Own code quality and standards for the systems you're responsible for, including CI checks (linting, tests, coverage)
• Work through the existing tech debt backlog, prioritising fixes that unblock SLA commitments
• Optimise pipelines for performance and cost as data volumes grow
• Handle the failure modes specific to energy data: late arrivals, reprocessing, backfills
• Build and improve observability across your systems so problems surface before they cause downstream impact
• Set up alerting tied to the SLAs the team is committing to
• Investigate and resolve issues within your domain, escalating genuinely cross-system problems
• Help scope and prepare the platform for new data sources tied to international expansion
• Flag where current architecture won't hold up as scope grows, even if fixing it isn't your call to make alone
• Work closely with the AI team as their data needs on the platform grow

• We move fast. Most processes take 2 to 3 weeks from first chat to offer. If you need us to adapt anything, let us know
• Intro call with Talent, 30 minutes
• Behavioural interview, 60 minutes
• Skills interview with cross-functional leaders, 60 to 90 minutes, including a practical exercise
• Bar Raiser interview with leadership stakeholders, 45 minutes