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Data Engineer
Gzira, Malta
Actively hiring, first seen by us 7 days ago- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingIngest diverse data sources in batch into bronze, silver and gold layers
Build and operate streaming pipelines from sources such as Kafka
Write unit, integration and data quality tests for every change
Monitor and troubleshoot Databricks pipelines and jobs day to day
From the employer’s posting
Ingest and integrate diverse data sources into the lakehouse in batch, following our bronze, silver and gold layering
design, build and operate streaming pipelines on Databricks from sources such as Kafka
Write automated tests for every change
Monitor and operate our cloud data platform (Databricks on Azure) day to day
Posting record
Report an errorActively hiringFirst seen by us 7 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 29, 20267 days ago
- Recorded sightings
- 7
- Last seen by us
- Nov 5, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Catena Media is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
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.
- Python
- SQL
- Azure
- Databricks
- Kafka
- Spark
- PySpark
Where the posting names them
YOUR CHALLENGE: - Design and develop data pipelines in Python (PySpark) on Databricks, applying engineering best practices for performance, reliability and maintainability. - Ingest and integrate diverse data sources into the lakehouse in batch, following our bronze, silver and gold layering and ensuring data quality and accuracy.
- Experience orchestrating pipelines with Databricks Workflows (Jobs) and deploying them with Databricks Asset Bundles; Azure Data Factory experience is an asset. - Coding proficiency in PySpark and Python. - A strong testing mindset: experience writing unit and integration tests (e.g. pytest) and data quality checks, and writing code that is designed to be tested.
- Hands-on experience with event streaming and real-time ingestion: Spark Structured Streaming and event platforms such as Kafka. - Strong SQL skills; experience with relational databases and complex queries. - Experience orchestrating pipelines with Databricks Workflows (Jobs) and deploying them with Databricks Asset Bundles; Azure Data Factory experience is an asset.
We are seeking a Data Engineer to join our Data & Analytics team, responsible for designing, building, testing and maintaining scalable data pipelines and data models on our Databricks lakehouse on Azure, governed through Unity Catalog. You will work in an AI-first team where agentic AI is part of how we design, build, test and operate our data platform every day. As part of the team, you’ll contribute to technical design and help shape our engineering standards. YOUR CHALLENGE:
- Use agentic AI tools (such as Claude Code) daily to build, test, review and troubleshoot, and help design and improve the AI agents and skills that automate our data workflows, with human review built in. - Monitor and operate our cloud data platform (Databricks on Azure) day to day: track pipelines and jobs, troubleshoot issues and support daily data operations. - Collaborate with engineers, analysts, and business stakeholders; support ad hoc data analysis requests.
- Proven experience as a Data Engineer, building and maintaining scalable ETL/ELT pipelines for data lakes or lakehouses, with exposure to both batch and real-time data. - Hands-on experience with Databricks on Azure, including Unity Catalog (catalogs, schemas, permissions and lineage). - Hands-on experience with event streaming and real-time ingestion: Spark Structured Streaming and event platforms such as Kafka.
- Strong SQL skills; experience with relational databases and complex queries. - Experience orchestrating pipelines with Databricks Workflows (Jobs) and deploying them with Databricks Asset Bundles; Azure Data Factory experience is an asset. - Coding proficiency in PySpark and Python.
- A strong testing mindset: experience writing unit and integration tests (e.g. pytest) and data quality checks, and writing code that is designed to be tested. - Solid software engineering practices: Git, pull requests and code review, CI/CD (Azure DevOps, GitHub Actions or equivalent) and documentation. - Hands-on experience working with agentic AI: using AI coding agents (such as Claude Code) in daily engineering work, and designing, building or integrating LLM-powered agents that orchestrate data workflows.
- Ingest and integrate diverse data sources into the lakehouse in batch, following our bronze, silver and gold layering and ensuring data quality and accuracy. - Help introduce event streaming and real-time ingestion to the platform: design, build and operate streaming pipelines on Databricks from sources such as Kafka, handling late, duplicate and out-of-order events reliably. - Maintain data models and the data estate codebase, with data governed, secured and documented in Unity Catalog.
- Write automated tests for every change: unit tests for pipeline logic, integration tests against real data, and data quality checks that catch issues before they reach users. - Follow and contribute to the team’s engineering standards: clean, modular code, peer code review, CI/CD, infrastructure as code (Databricks Asset Bundles) and clear documentation. - Use agentic AI tools (such as Claude Code) daily to build, test, review and troubleshoot, and help design and improve the AI agents and skills that automate our data workflows, with human review built in.
- Hands-on experience with Databricks on Azure, including Unity Catalog (catalogs, schemas, permissions and lineage). - Hands-on experience with event streaming and real-time ingestion: Spark Structured Streaming and event platforms such as Kafka. - Strong SQL skills; experience with relational databases and complex queries.
Your next step
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Demand for Data Engineer roles
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.
About the role
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.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
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.
Job description
We are seeking a Data Engineer to join our Data & Analytics team, responsible for designing, building, testing and maintaining scalable data pipelines and data models on our Databricks lakehouse on Azure, governed through Unity Catalog. You will work in an AI-first team where agentic AI is part of how we design, build, test and operate our data platform every day. As part of the team, you’ll contribute to technical design and help shape our engineering standards.
YOUR CHALLENGE:
- Design and develop data pipelines in Python (PySpark) on Databricks, applying engineering best practices for performance, reliability and maintainability.
- Ingest and integrate diverse data sources into the lakehouse in batch, following our bronze, silver and gold layering and ensuring data quality and accuracy.
- Help introduce event streaming and real-time ingestion to the platform: design, build and operate streaming pipelines on Databricks from sources such as Kafka, handling late, duplicate and out-of-order events reliably.
- Maintain data models and the data estate codebase, with data governed, secured and documented in Unity Catalog.
- Write automated tests for every change: unit tests for pipeline logic, integration tests against real data, and data quality checks that catch issues before they reach users.
- Follow and contribute to the team’s engineering standards: clean, modular code, peer code review, CI/CD, infrastructure as code (Databricks Asset Bundles) and clear documentation.
- Use agentic AI tools (such as Claude Code) daily to build, test, review and troubleshoot, and help design and improve the AI agents and skills that automate our data workflows, with human review built in.
- Monitor and operate our cloud data platform (Databricks on Azure) day to day: track pipelines and jobs, troubleshoot issues and support daily data operations.
- Collaborate with engineers, analysts, and business stakeholders; support ad hoc data analysis requests.
- Drive continuous improvement through training and self-development.
- Any other ad hoc tasks that may be required by the business from time to time.
TO DO IT, YOU WILL NEED:
- A Degree in IT, Computer Science, or related fields.
- Proven experience as a Data Engineer, building and maintaining scalable ETL/ELT pipelines for data lakes or lakehouses, with exposure to both batch and real-time data.
- Hands-on experience with Databricks on Azure, including Unity Catalog (catalogs, schemas, permissions and lineage).
- Hands-on experience with event streaming and real-time ingestion: Spark Structured Streaming and event platforms such as Kafka.
- Strong SQL skills; experience with relational databases and complex queries.
- Experience orchestrating pipelines with Databricks Workflows (Jobs) and deploying them with Databricks Asset Bundles; Azure Data Factory experience is an asset.
- Coding proficiency in PySpark and Python.
- A strong testing mindset: experience writing unit and integration tests (e.g. pytest) and data quality checks, and writing code that is designed to be tested.
- Solid software engineering practices: Git, pull requests and code review, CI/CD (Azure DevOps, GitHub Actions or equivalent) and documentation.
- Hands-on experience working with agentic AI: using AI coding agents (such as Claude Code) in daily engineering work, and designing, building or integrating LLM-powered agents that orchestrate data workflows.
- Comfortable delivering in a fast-paced, agile environment.
- Strong analytical and problem-solving skills.
- Excellent communication and team collaboration.
- Self-motivated with a drive for continuous learning.
Want to help build the future of Catena Media? Apply now for the opportunity to join our collaborative team!
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Gzira, Malta
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