Senior Data Engineer

Role: Senior Data Engineer / Data Stewardship Consultant

Location: Amsterdam, NL

Type: Contract

Role Overview

We are seeking a Data Engineering and Data Stewardship professional to support the development and management of customer-domain data products across a large-scale data environment.

The role sits at the intersection of data engineering, analytics engineering, data quality, and data governance . The consultant will contribute to building reliable and scalable data products, improving data quality, establishing governance standards, maintaining metadata and business definitions, and enabling stakeholders to effectively use and understand data.

Key Responsibilities

• Build, enhance, and maintain data ingestion and transformation pipelines using SQL and Python, including supporting ad-hoc analytics requirements.
• Develop and maintain data quality rules, automated tests, monitoring, and dashboards using tools such as dbt and data quality frameworks.
• Lead root-cause analysis of data quality issues and coordinate corrective actions with relevant stakeholders.
• Maintain business definitions, data harmonization rules, metadata, and documentation across datasets.
• Review data models, data contracts, and data mappings to ensure alignment with established data governance standards.
• Support the adoption of data governance practices and improve data discoverability, consistency, and usability.
• Present data product, data quality, and governance insights to technical and business stakeholders.
• Collaborate closely with data engineers, data stewards, software engineers, product teams, and other stakeholders.
• Contribute to continuous improvement of data products, processes, and governance practices.

Day-to-Day Activities

• Develop and maintain data pipelines and transformation workflows.
• Perform SQL-based data analysis and investigation.
• Define, implement, and monitor data quality rules and tests.
• Investigate data quality failures and drive resolution with relevant teams.
• Review and maintain data models, mappings, definitions, and metadata.
• Support data governance initiatives and standards.
• Prepare and present data insights and quality metrics through dashboards and collaboration tools.
• Work with engineering and product teams to translate business requirements into reliable and governed data products.

Technical & Analytical Skills

• Strong fundamentals in data engineering, including developing pipelines using dbt and/or Python .
• Experience writing production-grade code and deploying data solutions on cloud platforms, with GCP experience preferred .
• Hands-on experience with BigQuery and/or PySpark/Databricks . Experience with both is highly desirable.
• Strong SQL skills for data management, transformation, querying, and analysis.
• Practical experience with dbt for data modelling, transformation, and data quality testing.
• Understanding of data quality frameworks, including defining rules, developing tests, monitoring results, and investigating failures.
• Basic data visualization skills; experience with Looker Studio/Data Studio is desirable, while Power BI experience is a plus.
• Strong understanding of data structures, including data models, databases, data warehouses, and data flows across systems.
• Experience working with customer-related data, such as customer profiles, transactions, events, and interactions, is an advantage.
• Knowledge of data governance frameworks, metadata management, data cataloguing, and data stewardship practices is desirable.

Communication & Stakeholder Management

• Strong communication and collaboration skills when working with business, product, data, and software engineering teams.
• Ability to clearly present findings, analysis, and recommendations using dashboards, collaboration tools, and other visual formats.
• Strong problem-solving skills, with the ability to assess data issues, identify ownership, and drive resolution collaboratively.
• Ability to translate business requirements into data products, data quality rules, and governance standards.
• Comfortable working across technical and business domains and communicating complex data concepts clearly.

Key Competencies

The three most important capabilities for this role are:

1. Data Engineering Fundamentals
2. Ability to develop robust data pipelines using dbt and/or Python and deliver production-grade solutions on cloud platforms, preferably GCP.
3. SQL & Data Quality
4. Strong SQL and data management skills, combined with practical experience using dbt for data modelling and developing or evaluating data quality tests.
5. Proactive Mindset
6. A self-driven approach with the ability to operate effectively in an evolving environment, take ownership, identify opportunities for improvement, and confidently navigate ambiguity.