Budapest, Hungary
Open 4 months, first seen by us 131 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Design and build scalable ELT packages and data pipelines from source systems
Define the optimal architecture and logical and physical data models for the platform
Integrate and transform new internal and external data sources end to end
Manage and monitor data pipelines and resolve data interface incidents
Monitor data quality with a focus on consistency
designs and develops scalable ELT packages and routines from the source systems
Define the optimal architecture of the Data & Analytics platform
Integrate and transform new data sources (internal and external) by creating a full pipeline from ingestion to ETL process
Manage and monitor data pipelines to source and transform data to the Data & Analytics Platform(s)
Ensure and monitor high data quality standards with focus on data consistency
Master’s degree in IT / Economics
Excellent knowledge and proven professional experience with MS Azure and Lakehouse architecture
in-depth knowledge and/or certification in Databricks (Spark)
Fluent English
Qualifications - Master’s degree in IT / Economics - 5-6 years of data engineer experience
Open 4 monthsWe first saw this posting 131 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 131
Up longer than most. Similar jobs usually come down between day 6 and day 54.
Well past MET Group’s usual: its jobs come down by about day 63.
From 11,391 similar postings that came down, and 11 of MET Group’s. Coming down is not always a hire.
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Ability to think beyond technology requirements to build ONE logical Data & Analytics platform - Excellent knowledge and proven professional experience with MS Azure and Lakehouse architecture, in-depth knowledge and/or certification in Databricks (Spark) - Well structured, analytically thinking and demonstrating the ability to explain processes in a clear and understandable manner to a non-technical audience
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New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
PURPOSE OF THE POSITION
The Data Engineer is responsible for the maintenance and the improvement of all the data flows in the data analytics platform(s). The Data Engineer designs and develops scalable ELT packages and routines from the source systems in order to create information (datasets), defining and building the data pipelines.
The Data Engineer additionally analyzes complex data models and IT Applications, data flows, dependencies and relationships in order to contribute to conceptual physical and logical data models.
Moreover, the Data Engineer acts as architect of the Data & Analytics platform defining the logical data model and the physical data model(s). He/She also keeps up with industry trends and best practices, advising the management on new and improved data engineering strategies.
Main responsibilities
MET Group is an integrated European energy company, headquartered in Switzerland, with activities in natural gas and power, focused on multi‑commodity wholesale, trading and sales, as well as energy infrastructure and industrial assets.
The Group is represented in 24 countries: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Czech Republic, Croatia, France, Germany, Greece, Hungary, Italy, North Macedonia, Moldova, Poland, Romania, Serbia, Singapore, Slovakia, Spain, Switzerland, The Netherlands, Turkey and Ukraine.
MET is present in 33 national energy markets and 51 international trading hubs. The Group has a significant end‑consumer presence in Belgium, Croatia, Italy, Hungary, Romania, Slovakia, Spain, and The Netherlands.
The company has 1400+ permanent staff. The company is owned 90% by MET employees and 10% by Keppel Infrastructure, a wholly owned subsidiary of Keppel Corporation*.
* Listed on the Singapore Exchange
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Budapest, Hungary
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