Senior Azure Data Engineer

Description

The company is a global partner for AI-first digital transformation and engineering, with over 25 years of experience and about 5,000 staff in 16 countries. It builds scalable products, platforms and data-driven solutions.

Its focus areas are artificial intelligence, data and cloud, intelligent automation and digital products.

In the CEE region, a team of nearly 1,000 experts delivers applications, data platforms and AI solutions for clients including Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull and many others. The team works with AWS, Azure, GCP, Databricks and Snowflake, and shares knowledge across projects.

Responsibilities

• Build, test and deploy scalable data pipelines for reporting and analytics.
• Create new production-ready pipelines for priority reporting use cases.
• Review existing reporting pipelines and refactor them for reliability, performance, maintainability and observability.
• Keep new and existing pipelines running so data is delivered reliably every day.
• Own SRE practices for data pipelines and products.
• Diagnose data incidents and pipeline failures, find root causes and put lasting fixes in place.
• Turn reporting needs into solid technical solutions with data professionals, architects, product managers and engineering managers.
• Help onboard Core Products data workloads onto the Data Platform.
• Make sure pipelines match the Data Platform's architecture, governance, security and engineering standards.
• Meet platform standards for data modelling, code quality, testing, CI/CD, documentation, security and monitoring.
• Document data flows, dependencies, transformation logic and operating procedures so handover is smooth.
• Share knowledge and engineering practices with the Core Products data team to help it become self-sufficient over time.

Requirements

• At least 8 years of professional data engineering experience, including owning production-grade data pipelines.
• Databricks Data Engineer Professional certification.
• Strong, proven hands-on work with Microsoft Azure and Databricks.
• Advanced SQL and Python skills.
• A solid track record of designing, building and operating data pipelines and analytical data products.
• A good command of modern data platform and lakehouse principles, including medallion architecture and dimensional modelling for reporting.
• Experience with DataOps and software engineering practices: version control, automated testing, CI/CD, deployment, monitoring and incident management.
• Experience with SRE practices and ongoing operational improvement.
• Strong stakeholder communication and the ability to work across data, architecture, product and engineering teams.
• Data modelling skills and the ability to see the full picture of different data scopes and business objectives before deciding what to build.
• Able to work autonomously, make sound technical decisions and document them clearly.
• Experience with data governance, lineage and access control in Databricks, including Unity Catalog.
• Experience with payment or transaction data.
• Experience working in distributed international teams.

Conditions

The hiring process has five steps: CV review, HR call, interview, client interview and decision.