Data Engineering Manager at Metyis
At a glance
- Company
- Metyis
- Location
- Amsterdam, NL, Netherlands
- Work arrangement
- On-site
- Employment type
- Full-time
- Experience
- 5–10 years
- Working hours
- 40 hours
- Visa sponsorship
- Offered, according to the listing
- Posted
- Added to The Stack
✨ The Stack take on this Metyis job
- 🏢 This is Metyis's only job here right now
- 📊 6 Data Engineer jobs in the Netherlands in the last 60 days see them
- 🛂 Visa: Highly Skilled Migrant permit. 30 or older: €5,942 a month guide
- 💡 No salary shown. Ask Metyis if it is above the minimum.
Updated 2026-11-01, from our data and official sources.
About the role
Responsibilities:
- Lead and implement technical roadmaps for client data platforms, ensuring architecture coherence across ingestion, processing and analytics.
- Design and own end-to-end data pipelines, data hubs/lakes and data products (APIs, dashboards, automated services).
- Oversee development and operations using Python, Spark and Azure (Data Factory, Databricks, Synapse); enforce best practices and standards.
- Coach and develop engineers, promote technical excellence, and drive knowledge sharing across teams.
- Engage with stakeholders to evaluate business needs, prioritise delivery and ensure product outcomes.
Requirements:
- 5–7 years of hands-on experience in data engineering or similar lead roles, with production ETL/ELT and cloud platform experience.
- Strong SQL and data warehousing fundamentals; practical experience with Azure Data Factory, Databricks and Synapse preferred.
- Familiarity with orchestration (Airflow), containerisation (Kubernetes), RESTful APIs and data governance practices.
- Proven ability to define architecture, manage delivery and coach a small team; strong stakeholder and communication skills.
- Degree in a relevant technical discipline; prior people-management or team leadership experience is a plus.
Responsibilities
Lead and implement the technical roadmap for client data platforms, ensuring architecture cohesion across data ingestion, processing, and analytics. Manage end-to-end data pipelines and collaborate with data scientists to deliver data products, dashboards, and insights. Coach a small team to uphold engineering craftsmanship and continuous improvement while coordinating with stakeholders.
Requirements
5-7 years in a similar data engineering/lead role with hands-on experience building production ETL/ELT pipelines on Azure (Data Factory, Databricks, Synapse). Strong SQL, data warehousing principles, cloud architecture, and experience with Airflow, Kubernetes, and data governance. Bachelor's degree in a technical field; people management experience of a small team is a plus.
Key skills
- Python — in 1142 other listings on The Stack
- PySpark — in 37 other listings on The Stack
- SQL — in 512 other listings on The Stack
- ETL/ELT — in 18 other listings on The Stack
- Azure Data Factory — in 11 other listings on The Stack
- Azure Databricks — in 5 other listings on The Stack
- Azure Synapse — in 2 other listings on The Stack
- RESTful API — in 15 other listings on The Stack
- Airflow
- Kubernetes
- Data Governance
- Data Lakes
On The Stack (as of 2026-11-01)
- This is Metyis's only listing on The Stack.
- 56 visa-sponsored jobs in the Netherlands on The Stack, posted in the last 60 days.
- 39 visa-sponsored data engineer jobs in Europe on The Stack, posted in the last 60 days.
Frequently asked questions about Metyis
Is this Data Engineering Manager role still open?
It was posted on 2026-10-31 and is listed as active on The Stack. Confirm on Metyis's application page before applying.
Does Metyis offer visa sponsorship?
This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Metyis's site.
Where is this role located?
The listing gives the location as Amsterdam, NL, Netherlands. It is marked on-site.
What skills does this role ask for?
The listing names: Python, PySpark, SQL, ETL/ELT, Azure Data Factory, Azure Databricks, Azure Synapse, RESTful API, Airflow, Kubernetes, Data Governance, Data Lakes.