Data Engineer (Azure Synapse Analytics)

Job Description

Overview

We are seeking a motivated and detail-oriented Data Engineer to join our Data Engineering team. This role will focus on designing, building, and maintaining enterprise-grade data infrastructure and ETL pipelines using Azure Synapse Analytics. You will collaborate with senior engineers, data scientists, and cross-functional teams to deliver scalable, high-performance data solutions that power our analytics and reporting capabilities.

Key Responsibilities

• Design, develop, and optimize ETL pipelines and data workflows using Azure Synapse Analytics, Azure Data Factory, and related Azure services.
• Build and maintain scalable data models using the Kimball methodology to support business intelligence, analytics, and reporting requirements.
• Consume and integrate data from diverse sources, including relational databases, NoSQL databases, event-based systems, and data lakes.
• Collaborate with stakeholders in engineering, analytics, and IT to understand data needs and deliver robust solutions.
• Ensure data quality, integrity, and security across pipelines and storage systems.
• Implement best practices for data governance, version control (e.g., Git), and CI/CD for data workflows.
• Participate in technical standups, sprint planning, and team syncs to align on priorities and deliverables.
• Monitor and optimize pipeline performance, identifying and resolving bottlenecks.
• Contribute to documentation and knowledge sharing within the team.
• Participate in an on-call rotation to monitor and support critical data pipelines and workflows, ensuring they meet service level agreements and maintain high availability.

Required Qualifications

• 2–4 years of experience in data engineering or a related field.
• Proficiency in SQL and Python for data processing, transformation, and scripting.
• Hands-on experience with Azure Synapse Analytics, Azure Data Factory, or similar cloud-based data platforms.
• Experience with the Kimball methodology for designing dimensional data models (e.g., star schema, snowflake schema).
• Familiarity with consuming data from relational databases (e.g., Azure SQL Database, AWS RDS), NoSQL databases (e.g., Cosmos DB, DynamoDB), event-based systems (e.g., Event Hubs, Kafka), and data lakes (e.g., Azure Data Lake).
• Experience with version control systems (e.g., Git) and CI/CD pipelines for data engineering workflows.
• Exposure to other Azure services (e.g., Azure Data Lake, Azure SQL Database, or Databricks) is a plus.
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
• Excellent communication skills to articulate technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with other cloud platforms (e.g., AWS) and their data tools.
• Familiarity with data visualization tools (e.g. Power BI) for reporting purposes.
• Understanding of data security and compliance standards (e.g. CCPA).