De positie

Algorithmic Trading Platform | Greenfield ML Engineering | Databricks Ecosystem

At Eneco, technology is becoming one of the key differentiators in how energy is traded, forecasted, optimized, and delivered. The trading landscape is evolving rapidly towards real-time decision making, autonomous forecasting, streaming intelligence, and scalable machine learning systems.

To accelerate this transformation, Eneco is investing heavily in next-generation data and AI platforms that support mission-critical trading operations across European energy markets.

This is not a traditional “maintain existing pipelines” role.

This is a greenfield engineering opportunity where you will help shape the foundations of a modern ML-driven trading ecosystem; from strategy lifecycle management and simulation frameworks to scalable production deployment and real-time monitoring.

You will work in a highly technical environment alongside quantitative traders, ML engineers, platform engineers, and data specialists to transform experimental trading ideas into robust, production-grade systems that operate at scale.

The environment combines:

• Real-time and streaming data platforms
• Low-latency processing
• Databricks-native MLOps
• Large-scale time-series systems
• Automated deployment pipelines
• Scalable cloud infrastructure
• Advanced forecasting and optimization models

The focus is not only on building models, but on engineering reliable systems that can continuously evolve, scale, and create measurable commercial impact.

Your Mission

As a Senior Machine Learning Engineer, you will take ownership of the full strategy lifecycle for algorithmic and ML-driven trading solutions.

You will bridge the gap between quantitative research and production engineering by designing reusable frameworks, scalable deployment patterns, and high-performance ML infrastructure that enable trading strategies to move safely and efficiently from experimentation into live environments.

This role is ideal for engineers who enjoy combining:

• software engineering discipline,
• ML systems design,
• cloud-native architecture,
• and real-world business impact.

You will have significant influence on technical direction, engineering standards, tooling, and platform maturity.

What Makes This Opportunity Unique

Greenfield Engineering

You won’t inherit rigid legacy systems and endless maintenance work. You’ll help design and build the next generation of ML-driven trading infrastructure from the ground up.

High Ownership & Technical Influence

You are trusted to challenge existing ideas, introduce new engineering patterns, and help define the future technical roadmap.

Real Impact

Your systems will directly support trading decisions, forecasting capabilities, and optimization strategies in the energy market.

Advanced Databricks Ecosystem

Eneco is one of the more mature Databricks environments in the Netherlands, actively investing in:

• MLOps,
• streaming platforms,
• Asset Bundles,
• scalable forecasting frameworks,
• and platform standardization.

Mission-Driven Domain

You contribute to the acceleration of smarter and more sustainable energy systems while working on technically complex engineering challenges.

Over het bedrijf

Eneco is one of the leading energy companies in the Netherlands and a major player in accelerating the transition towards a more sustainable and intelligent energy ecosystem.

Within Eneco’s trading and technology landscape, large-scale data platforms, machine learning, forecasting, and real-time decision systems play a critical role in optimizing energy flows and trading operations across European markets.

The organization is heavily investing in cloud-native AI platforms, streaming architectures, Databricks ecosystems, and scalable ML engineering capabilities, creating a highly innovative environment where engineering, data, and business impact come together.

Wat breng jij

Strategy Lifecycle Engineering

Transform research notebooks and experimental strategies into scalable, production-grade trading components

• Design reusable frameworks for:
  • backtesting,
  • simulation,
  • validation,
  • deployment,
  • and monitoring
• Ensure reproducibility across research, testing, and live trading environments
• Build versioning, rollback, and controlled rollout mechanisms for trading strategies
• Develop engineering standards that improve reliability and speed of delivery

Real-Time ML & Data Infrastructure

Build and optimize feature pipelines for trading strategies and forecasting models

• Work with streaming and time-series datasets in low-latency environments
• Ensure point-in-time correctness and data consistency in simulations
• Collaborate with platform and data engineers on:
  • market data ingestion,
  • event-driven architectures,
  • semantic layers,
  • orchestration,
  • and scalable compute patterns

You’ll work within a modern ecosystem centered around:

• Databricks
• Spark
• Python
• Terraform
• CI/CD
• Azure
• Streaming orchestration
• Lakehouse architecture

Productionization & MLOps

Package and deploy ML and quantitative strategies into production environments

• Improve runtime performance, scalability, and reliability
• Build automated safeguards and validation checks before live execution
• Standardize deployment patterns and reusable ML engineering workflows
• Contribute to the evolution of Eneco’s ML platform capabilities

This environment strongly values:

• infrastructure-as-code,
• automation-first engineering,
• reproducibility,
• observability,
• and platform thinking.

Monitoring, Reliability & Trading Resilience

Build monitoring and observability capabilities for live strategies

• Detect drift, anomalies, and performance degradation
• Support incident analysis and post-mortem investigations
• Help improve operational excellence within a mission-critical trading environment

You’ll operate in a landscape where reliability and engineering quality directly influence commercial trading outcomes.

What We’re Looking For

Strong Engineering Foundation

Advanced Python engineering skills

• Experience designing scalable software systems
• Strong understanding of software architecture and production engineering

Databricks & ML Platform Experience

Hands-on experience with Databricks in production environments

• Experience with Spark and distributed processing
• Experience with CI/CD and infrastructure automation

ML Productionization Expertise

Experience operationalizing ML or quantitative models

• Strong understanding of reproducibility, validation, and observability
• Experience building reusable ML engineering patterns and frameworks

Time-Series / Trading / Real-Time Systems

Experience in one or more of the following is highly valuable:

• algorithmic trading,
• energy trading,
• forecasting,
• streaming systems,
• or low-latency environments.

Mindset

We are looking for engineers who:

• think in systems,
• care about engineering quality,
• enjoy solving complex problems,
• and want to build platforms that scale.

You combine strategic thinking with hands-on execution.