Senior Data Engineer - Trading Rotterdam • Eneco

• Designing and implementing data ingestion pipelines and ETL workflows for a variety of trading data sources, such as market prices, order-book information, executed trades, weather data, and renewable energy metrics. 

• Designing efficient data models and information models to support portfolio management, market analytics, market modeling, and algorithmic trading initiatives. Developing and maintaining robust databases to implement and operationalize these models. 

• Working alongside other engineers to design and implement comprehensive, end-to-end solutions for analysis, modelling, trading, and portfolio management, combining expertise to deliver cohesive and innovative outcomes.  

• Collaborating with stakeholders and cross-functional teams to tailor and scale trading data systems, while providing technical guidance to support analytics, decision-making, and business goals. 

• Ensuring all data solutions are built to be reliable, scalable, and adaptable, ready to support evolving business needs and future market developments. 

Must have:  

• Demonstrated expertise in designing, optimizing, and implementing large-scale data pipelines and ETL workflows, enforcing data quality, data governance and data modelling standards. 

• Software development best practices, including API design and development, code reviews, version control, automated testing, and CI/CD pipelines, with an interest in DevOps and SRE principles for reliable production deployments. 

• Our stack includes Python for data processing on Databricks, Snowflake, Kubernetes, DBT, Grafana for visualizations, and we primarily run on Azure. Proficiency in most of these tools is required, including advanced features. 

Nice to Have:  

• Experience designing and maintaining ML model deployment pipelines, leveraging MLOps practices in Databricks to automate model versioning, testing, deployment, and monitoring at scale. 

• Familiar with key DS and ML processes and techniques, such as data cleaning and preprocessing, feature engineering, feature selection, model training and tuning, model evaluation, cross-validation, hyperparameter optimization, model deployment, and monitoring model performance in production environments. 

• Skilled in energy trading, building and optimizing data pipelines for algorithmic trading, integrating diverse market data, using standard connectivity and backtesting methods, and partnering with traders and quants to deliver robust, scalable solutions