Description

Build and maintain scalable data pipelines and warehouse solutions using Azure services, with a focus on automation and engineering standards.

This role is on-site.

Responsibilities

• Design and implement complex stored procedures, views, and functions in SQL, adhering to team naming and header standards.
• Develop reusable Azure Data Factory pipelines with proper parameterization and dataset configurations.
• Process data using Python and PySpark, including dataframe manipulation and API interfacing.
• Maintain work items in Azure DevOps, ensuring status, notes, and linked artifacts are current.
• Conduct thorough peer code reviews via Git workflows, enforcing engineering standards and branching conventions.

Required Skills

• 3–6 years of data engineering experience in a technology-first environment.
• Strong SQL proficiency; T-SQL experience is a plus.
• Experience with cloud data warehousing platforms such as Databricks or Snowflake.
• Proficiency in Python, specifically PySpark and working with dataframes.
• Hands-on experience with Azure Data Factory for building and managing pipelines.
• Experience with API integration and interfacing with external services.
• Ability to document technical decisions and communicate clearly.

Preferred Skills

• Experience with CI/CD and DevOps release pipelines using YAML configurations.
• Work with AI/MCP tooling to connect data assets to LLM workflows.
• Background in web application, infrastructure, or networking.

Key Skills

Education

Any Gradute