Enode - Data Engineer

• Deliver Flex-critical data needs: build and maintain reliable pipelines and datasets that enable Flex models (e.g., demand/availability signals, aggregations, monitoring). • Deliver Flex-critical data needs • Evolve the data platform: assess what we have today and drive pragmatic improvements in architecture, tooling, and operating practices. • Evolve the data platform • Own data quality and trust: implement testing, lineage/definitions, and guardrails (e.g., dbt tests, anomaly detection, freshness checks) so stakeholders can trust outputs. • Own data quality and trust • Enable self-serve analytics: produce well modelled datasets and documentation that make it easy for others to answer questions without bespoke work. • Enable self-serve analytics • Partner on data science work: collaborate on data readiness for modelling, feature pipelines, evaluation workflows, and productionization concerns (even if you’re not the primary model builder). • Partner on data science work • Make high-leverage tech choices: propose and justify changes (or non-changes) to tools and processes, prioritizing impact and delivery over long platform rewrites. • Make high-leverage tech choices • This role will be right for you, if: • You’re a data engineer who likes shipping product outcomes, or a data scientist with strong engineering instincts who gets satisfaction from making pipelines stable, data trustworthy, and platforms scalable. • You enjoy working close to “real” problems: messy upstream inputs, ambiguous requirements, and high-stakes delivery. You can zoom in (fix a broken pipeline, optimize a model feature dataset) and zoom out (define what the platform should look like in 6–12 months). • You’re comfortable being a co-owner in a small team: you bring structure, propose priorities, and drive execution without needing heavy process. You care about impact, and you balance pragmatism with good engineering judgment. • You’re also excited about AI as a tool - whether that’s using it to speed up development, improve data quality workflows, or explore new ways of building and maintaining data products. • Strong data engineering background: you’ve built or evolved data pipelines/platforms in production (ideally in a startup/scale-up). • Pragmatic platform mindset: you can “improve the plane while flying”—make incremental improvements, manage trade-offs, and prioritize delivery. • Ownership + proactivity: you identify what matters, push work forward, and can operate effectively in a small team with high autonomy. • Solid SQL + data modeling skills: comfortable designing datasets that are accurate, maintainable, and useful for analytics/modeling. • Reliability instincts: you care about monitoring, data quality, testing, and operational excellence (not just building new pipelines). • Clear communication: you can collaborate with engineers, product, and business stakeholders, and translate needs into concrete data work. • Familiarity with parts of the modern data stack; dbt, data warehousing ELT tools (Snowflake, Airbyte) and BI tools • Experience supporting forecasting & time-series use cases (or high-volume event data). • Interest in (or exposure to) energy markets / flexibility / trading. • Experience designing data access, governance, and cost-aware warehouse usage in a growing org. • Strong interest in AI, with evidence of experimenting (LLM-assisted workflows, automation, evaluation, etc.).