Data Engineer

Description

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.

Requirements

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.

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