Staff Full-Stack Data Engineer

At Eneco, we are committed to accelerating the energy transition. Through our One Planet strategy, we aim to become climate neutral by 2035 - together with our customers.

As a Staff Full-Stack Data Engineer, you’ll play a key role in making this ambition a reality. By building and modernizing the data platforms that power energy trading, forecasting and critical business operations, you’ll help shape the technology behind a more sustainable energy system. You’ll work on complex, large-scale engineering challenges, collaborate across teams and have the technical influence to drive lasting improvements in how we build and operate data systems.

Eneco is accelerating the energy transition, and resilient, high-quality data systems are foundational to that mission. Across Eneco, business-critical activities-from managing energy and serving customers to supporting corporate functions-rely on reliable, well-architected, and scalable data platforms.

As a Staff Data Engineer, you will operate across multiple engineering domains, collaborating with various platform teams and supporting business-critical initiatives. You will tackle some of the most complex technical challenges in the energy value chain: stabilizing and re-architecting critical data systems, coordinating large engineering projects, improving data lineage and governance, and unblocking projects that impact multiple engineering and business teams.

This is a hands-on technical leadership role, not a conceptual “architect”. You will design, build, review, decide, and guide. You will work closely with Engineering Managers, Staff Engineers, and Product Managers to drive technical clarity and consistent engineering practices across the organization.

Technical Leadership Across Domains

• Driving large-scale system and architecture initiatives across long-term energy planning, long- and short-term trading, including data flows, integration with neighboring systems (provisioning, asset management, reporting), and the ongoing evolution of the data landscape.

• Stepping in to unblock or lead complex engineering efforts-for example, clarifying and redesigning lineage for datasets in a domain, fixing systemic data quality issues, and improving cross-team integrations.

• Shaping and reviewing technical designs, ensuring robustness, scalability, and alignment with Eneco’s enterprise architecture.

Hands-On Engineering & System Design

• Leading the design and implementation of data and ML platforms and pipelines using Databricks, Delta/Unity Catalog, Airflow, dbt, Snowflake, Azure, and related services, applying best practices in data modeling, orchestration, storage, and streaming.

• Modernizing and refactoring legacy data and ML workflows to enhance reliability, transparency in data lineage, maintainability, and management of metadata, lineage, content, and master data.

• Designing and building systems that integrate data and ML pipelines with other platforms and user-facing products, while applying engineering best practices in DevOps, CI/CD, infrastructure as code, observability, and incident management.

• Building, deploying, and running AI agents as part of engineering workflows, extending your own and teams’ delivery capacity through task delegation and automation, while ensuring reliable results.

Platform & Governance Alignment

• Improving coherence and standardization across teams by promoting common patterns for ingestion, modeling, streaming/batch processing, feature engineering, ML model development, experimentation, testing, and serving.

• Ensuring solutions meet Eneco’s standards for security, compliance, and operational resilience.

Mentorship, Enablement & Cross-Team Collaboration

• Advising and supporting Engineering Managers and Senior Engineering Managers in technical decision-making (e.g., framework selection, architectural direction, build-vs-buy decisions).

• Assisting with planning topics such as team sizing, hiring needs, and sequencing of technical roadmap items.

• Sharing best practices, running design reviews, and coaching engineers to level up the engineering bar across eneco.

Must-Haves

• 8+ years of experience in data engineering or software engineering, with at least 3 years in a senior or staff role driving multi-team technical decisions.

• Deep hands-on expertise with Databricks, Delta Lake/Unity Catalog, Airflow, Snowflake, dbt, and Azure cloud services.

• Demonstrable experience designing, operating, and stabilizing mission-critical, distributed data systems where reliability, correctness, and continuity are essential requirements. Including end-to-end data pipelines, event-driven architectures, and platform integrations.

• Strong understanding of data architecture, data modeling, streaming, data lineage, data quality frameworks, and metadata management.

• Good knowledge across ML engineering, including feature engineering, model lifecycle management, experimentation, performance monitoring, and MLOps.

• Solid knowledge of software engineering and DevOps fundamentals, including CI/CD, infrastructure as code, observability, containerization, API design, and operational excellence.

• Hands-on experience building, deploying, and running AI agents to accelerate engineering work. Ability to scale this impact through reusable agent workflows, AI-ready data models, and integration patterns.

• Experience with timeseries analytics environment (databases, analytics, processing and modelling).

• Ability to drive alignment across engineering and product teams, especially in ambiguous or high-impact problem spaces.

• Excellent written and verbal communication skills, with the ability to translate technical topics for both engineering and business stakeholders.

Nice to have

• Experience in energy markets, commodity trading, or near-real-time operational systems.

• Experience with forecasting platforms, ML lifecycle design, and integrating data & ML systems into production.

• Familiarity with high-frequency or low-latency data processing patterns.

You’ll join Eneco’s Data & AI organisation, working across our energy trading and forecasting domains. You’ll provide technical leadership in designing, building and improving the data platforms and pipelines that power energy trading, forecasting and long-term energy planning. Working closely with engineering and product teams, you’ll ensure critical data flows are reliable, scalable and well-integrated, while driving technical improvements across multiple domains.

Contact our recruiter at: [email protected]

• Lead complex, cross-domain data engineering initiatives across energy trading, forecasting and long-term energy planning.

• Design, build and modernize mission-critical data and ML platforms, using Databricks, Azure, Snowflake and modern engineering practices.

• Combine hands-on technical leadership with AI agent automation, driving engineering excellence, scalability and reliability across teams.