Data Engineer

We are looking for a Data Engineer to join a confidential enterprise organization in McKinney, Texas and help advance a large-scale cloud data environment. This contract opportunity with potential for a permanent role focuses on building and refining a modern lakehouse platform in Azure, with a strong emphasis on Azure Databricks and scalable data engineering practices. The role will work across technical and business teams to create dependable, secure, and high-performing data solutions that support analytics and decision-making.

Responsibilities:
• Create and maintain robust data pipelines in Azure Databricks using Spark-based engineering approaches to support large-volume processing needs.
• Build ingestion frameworks that bring data from APIs, event streaming platforms, cloud storage, and enterprise systems into structured lakehouse layers.
• Apply lakehouse design standards across bronze, silver, and gold environments to improve data usability, consistency, and downstream reporting value.
• Enhance platform efficiency by tuning Spark workloads, refining storage strategies, and improving processing performance and cost management.
• Connect Databricks solutions with Azure services such as Data Factory, Synapse, Data Lake Storage, Event Hub, Key Vault, and Azure DevOps.
• Establish secure data access patterns through governance controls, identity-based access, and protection measures such as masking and policy alignment.
• Partner with architects, analysts, data scientists, and business stakeholders to deliver trusted datasets, metrics, and KPI-ready data assets.
• Strengthen data reliability by implementing validation checks, monitoring, lineage tracking, alerting, and recovery processes for critical workflows.
• Contribute technical leadership through code reviews, engineering guidance, and mentoring support for other members of the data team.• Bachelor’s degree in Computer Science, Engineering, or a related discipline, or equivalent hands-on experience.
• At least 5 years of data engineering experience, including 3 or more years working with Azure Databricks and Apache Spark using PySpark.
• Strong knowledge of Delta Lake, Unity Catalog, Databricks notebooks, and cloud-based data warehousing concepts, including Snowflake-related environments.
• Hands-on experience with Azure Data Lake Storage, Event Hub, Azure Data Factory, Key Vault, Synapse, and Microsoft Fabric.
• Advanced programming skills in Python and SQL, with additional exposure to Scala considered beneficial.
• Solid understanding of dimensional modeling, star and snowflake schema design, and data platform performance optimization.
• Experience supporting CI/CD and deployment automation for Databricks environments using tools such as Azure DevOps, Terraform, or related frameworks.
• Familiarity with data governance, compliance, and security standards, along with exposure to real-time data pipelines, reporting platforms, or ML-enabled workflows.