Principal Full-Stack Data Engineer

At Eneco, we are accelerating the energy transition. With our ambitious One Planet Plan, we aim to be the first energy company to become climate neutral by 2035. That means no more reliance on fossil fuels —and a full focus on smarter, cleaner, and more resilient energy systems.

Achieving this requires data platforms that are reliable at scale and capable of supporting decisions across the entire energy value chain, from long-term forecasting and asset planning to near-real-time trading and balancing. As a Principal Full Stack Data Engineer, you will work on the systems that make this possible, modernizing and stabilizing mission-critical data and ML platforms, improving data quality and lineage, and setting technical standards across teams.

At Eneco, you combine deep technical ownership with real-world impact, working alongside strong engineering and product peers in an environment where engineering excellence directly supports both the energy system and the transition to a sustainable future.

Eneco is accelerating the energy transition, and resilient, high-quality data systems are foundational to that mission. The energy balancing chain—from long-term forecasting, asset modeling and planning, all the way to short-term intraday trading—relies on reliable, well-architected, and scalable data platforms.

As a Principal 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.

• 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.

• 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 Dev Ops, CI/CD, infrastructure as code, observability, and incident management.

• 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.

• 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…