Data Engineer (Azure Databricks) | 1-Year Contract

We are looking for a hands-on, execution-driven Data Engineer to join our team on a contract basis. In this role, you will work directly alongside our Lead Data Engineer and Analytics Engineers to build and maintain scalable cloud data pipelines, establishing robust bronze and silver layers within our Medallion Architecture to support strategic BI initiatives (Power BI & Sigma). This position offers a balanced split between new pipeline engineering, quality assurance, operational troubleshooting, and ad-hoc data support.

Key Responsibilities:

Pipeline Engineering & Data Quality (60%)

• Design, develop, and deploy robust ETL/ELT pipelines using Azure Databricks.
• Build and optimize Bronze (Raw) and Silver (Cleaned & Conformed) layers within a Medallion architecture.
• Implement automated data quality checks and testing frameworks to ensure data accuracy, reliability, and consistency.
• Partner with Analytics Engineers to deliver trusted datasets for downstream consumption.
• Monitor, troubleshoot, and enhance existing Azure Databricks workflows and legacy data pipelines.
• Resolve pipeline failures, manage schema changes, and optimize PySpark and Spark SQL performance.
• Ensure data processing meets operational and business SLA requirements.

Analytics Support & Stakeholder Collaboration (10%)

• Investigate and resolve data discrepancies through root-cause analysis.
• Support ad-hoc business data requests and operational reporting needs.
• Collaborate closely with Analytics Engineers to enable data models and reporting solutions in Power BI and Sigma.

What We're Looking For

Must-Have Skills & Experience

• 2-4 years of hands-on Data Engineering experience building and supporting production data pipelines.
• Strong experience with Azure Databricks, PySpark, and Spark SQL.
• Proven experience implementing Bronze and Silver layers within a Medallion architecture.
• Advanced SQL skills, including complex transformations, performance optimization, and window functions.
• Experience with data testing, QA practices, and automated data validation.
• Familiarity with BI tools such as Power BI or Sigma.
• Experience with Git, version control, and CI/CD best practices.

Nice to Have

• Experience working with dbt and Analytics Engineering teams.
• Exposure to workflow orchestration tools such as Apache Airflow.
• Knowledge of cloud storage and data services across Azure, AWS, or GCP (e.g., ADLS Gen2, Amazon S3, or Google Cloud Storage).

SSP are proud to be an equal-opportunity employer that seek to recruit and retain the most talented individuals from a variety of backgrounds, skills and perspectives.

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