Data Engineer Level 2

LeadStack Inc. is an award-winning, one of the nation's fastest-growing, certified minority-owned (MBE) staffing services provider of contingent workforce. As a recognized industry leader in contingent workforce solutions and Certified as a Great Place to Work, we're proud to partner with some of the most admired Fortune 500 brands in the world.

Job Description :

We are seeking a highly skilled Senior Azure Data Engineer to join our growing data team. In this role, you will be responsible for designing, building, and optimizing scalable data pipelines and architectures using Microsoft Azure services. You will collaborate closely with data scientists, analysts, and business stakeholders to ensure data is accessible, reliable, and efficiently processed.

5+ years of experience in data engineering or a related field, with at least 2+ years focused on Azure cloud technologies.

Deep experience with Azure Data Factory, Databricks, Azure Synapse, and Azure Data Lake Storage.

Proficient in Python and PySpark for data manipulation and processing.

Strong expertise in SQL and data modeling principles.

Solid understanding of cloud architecture, data warehousing concepts, and distributed systems.

Experience with DevOps practices, CI / CD pipelines, and version control (e.g., Git) is a plus.

Excellent communication and collaboration skills.

Key Responsibilities :

Design, build, and maintain scalable data pipelines and ETL processes using Azure Data Factory, Databricks, and PySpark.

Architect and implement cloud-based data solutions on Azure, leveraging services such as Azure Synapse Analytics, Azure Data Lake Storage, and Azure SQL.

Develop and optimize complex SQL queries and stored procedures for data transformation and reporting.

Work with structured and unstructured data to create clean, reusable datasets for analytics and business intelligence.

Monitor, troubleshoot, and improve the performance and reliability of data workflows and pipelines.

Ensure data quality, governance, and security standards are consistently met.

Collaborate with data scientists and analysts to support ML and AI initiatives.

Contribute to best practices, documentation, and knowledge sharing within the data engineering team.

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