Data Engineer

Catena Operations Ltd.; Malta (MT); On-site; USD 90,000 - 140,000; Full time

Job summary

Catena Operations Ltd. is seeking a Data Engineer to join our Data & Analytics team. You will design, build, test and maintain scalable data pipelines and models on our Databricks lakehouse on Azure, governed by Unity Catalog.

You will work in an AI-first environment, contribute to technical design, implement streaming data, ensure data quality, tests and documentation, and collaborate with engineers, analysts and stakeholders.

Qualifications

• Degree in IT, Computer Science, or related field.
• Proven experience building and maintaining scalable ETL/ELT pipelines for data lakes or lakehouses, with batch and real-time data.
• Hands-on Databricks on Azure experience, including Unity Catalog (catalogs, schemas, permissions, lineage).
• Experience with event streaming and real-time ingestion: Spark Structured Streaming and Kafka.
• Strong SQL skills and complex queries.
• Experience with Databricks Workflows and deploying with Databricks Asset Bundles; Azure Data Factory is a plus.
• Proficient in PySpark and Python.
• Solid software engineering practices: Git, PRs, code review, CI/CD, and documentation.
• Hands-on experience with agentic AI tools in daily engineering work.
• Comfortable in a fast-paced, Agile environment.
• Strong analytical and problem-solving skills.
• Excellent written and spoken communication and teamwork.
• Self-motivated with a drive for continuous learning.

Responsibilities

• Design and build data pipelines in Python (PySpark) on Databricks with performance and reliability in mind.
• Ingest diverse data sources into the lakehouse, following bronze/silver/gold layering and ensuring data quality.
• Develop streaming pipelines for real-time ingestion using Kafka and Spark Structured Streaming.
• Maintain data models and the data estate with Unity Catalog governance.
• Write unit and integration tests and data quality checks for code changes.
• Follow engineering standards: modular code, peer review, CI/CD, IaC (Databricks Asset Bundles) and documentation.
• Leverage AI tools to design, test, and troubleshoot data workflows with human review.
• Monitor Databricks on Azure, track pipelines, troubleshoot issues, support daily data operations.
• Collaborate with engineers, analysts, and stakeholders; handle ad hoc data analysis requests.
• Pursue continuous learning and contribute to process improvements.
• Perform other tasks as required by the business.

Skills

ETL pipelines

SQL

Python

PySpark

Testing mindset

CI/CD

Agile environment

Analytical thinking

Communication

Team collaboration

Problem solving

AI tooling

Education

Degree in IT/Computer Science or related field

Tools

Databricks on Azure

Unity Catalog

Kafka

Spark Structured Streaming

Azure Data Factory

Git

GitHub Actions

Azure DevOps

Databricks Workflows

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.