Data Engineer

CATENA OPERATIONS LIMITED

Malta, MT, United States

15 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.indeed.com

Tech stack

Artificial Intelligence Data Analysis Automation of Tests Unit Testing Microsoft Azure Code Review Computer Programming Continuous Integration Data Validation Data Infrastructure Extract Transform Load (ETL) Relational Databases

+25 more

Database Queries Github Python (Programming Language) DataOps Data Streaming Cloud Platform System Azure Data Factory Large Language Models Claude Code AI Coding Agents Agentic-AI Git Pytest Data Lakes Pyspark Integration Tests Infrastructure Automation Frameworks Information Technology Data Analytics Real Time Data Apache Kafka Spark Streaming Software Coding Data Pipelines Databricks

Job description

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. *

Requirements

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.

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