Data Engineer

Data Engineer – Azure / Microsoft Fabric

Role Summary

As a Data Engineer with 4–5 years of experience, you will design, implement, and maintain scalable, production‑grade data pipelines and data platform solutions. Your work will deliver reliable, high-quality data for analytics, reporting, ML/AI initiatives — enabling data-driven decision making across the business.

What You’ll Do

• Build, deploy and manage end‑to‑end ETL / ELT pipelines using Azure Data Factory (ADF), Azure Databricks (ADB) / Microsoft Fabric.
• Ingest data from diverse sources, transform, clean and store it in Azure Data Lake / OneLake or Delta Lake / Lakehouse, enabling downstream analytics and reporting.
• Design and maintain robust data models, warehouse / lakehouse schemas, ensuring data integrity, reliability and performance.
• Write efficient data transformation and processing logic using PySpark, Python and/or Scala, optimizing for large‑scale data workloads.
• Collaborate with data scientists, analysts, product owners, and other stakeholders to understand data needs and deliver appropriate solutions.
• Implement data governance, quality, and security standards; ensure compliance and data reliability across data lifecycle.
• Apply version control and CI/CD practices to pipelines and infrastructure (e.g. Git / Azure DevOps), supporting seamless deployment, monitoring and maintenance.
• Troubleshoot, monitor, and optimize data pipelines to ensure performance, scalability and operational excellence.

Required Skills & Experience

• 4–5 years of hands-on experience as a Data Engineer working with Azure Data Factory (ADF), Azure Databricks / Microsoft Fabric, and data‑lake / lakehouse storage (Azure Data Lake / OneLake / Delta Lake).
• Strong programming skills in PySpark, Python, and/or Scala; ability to write clean, efficient, maintainable code for large‑scale data processing.
• Proficiency in data modeling, data‑warehouse / lakehouse architecture and schema design.
• Solid understanding of ETL/ELT patterns, orchestration, scheduling and data pipeline lifecycle.
• Experience with MS SQL (or equivalent relational databases / data stores) for data storage or warehousing.
• Familiarity with version control (Git) and CI/CD pipelines for data engineering workflows.
• Problem-solving mindset, with ability to work on large datasets and meet data reliability, performance, and quality requirements.

Preferred / Nice‑to‑Have

• Exposure to event-driven or real-time data ingestion and processing (e.g. using messaging or streaming services).
• Familiarity with serverless or micro‑service style Azure components (e.g. Azure Functions, Logic Apps, Event Hub / Service Bus).
• Basic knowledge of reporting/BI tools (e.g. Power BI) — to aid end-to-end data-to-insight workflows.
• Experience with infrastructure‑as‑code / cloud‑infrastructure provisioning tools (e.g. Terraform / Bicep) and metadata/governance tools or processes.
• Experience or interest in building data governance, cataloging, lineage, and compliance standards.

Why You Might Be a Great Fit

• You enjoy working with large-scale data, solving complex data‑architecture challenges, and building data platforms that scale.
• You value writing clean, maintainable code and building pipelines that are robust, efficient, and reliable.
• You appreciate collaboration — working with analysts, data scientists, product teams to turn raw data into actionable insights.
• You stay updated on cloud-native data technologies, big data patterns, and best practices, and you take initiative to learn and implement new technologies as appropriate.

Bonus / Preferred Qualifications

• Certifications such as Azure Data Engineer Associate (or equivalent) or other Azure / cloud-data certifications
• Prior experience working in agile / scrum teams or global delivery environments
• Exposure to data governance, compliance, and security practices in enterprise data environments

Ready to make an impact with data? Apply now