Sr Azure Data Engineer Jobs in Texas (NOW HIRING)

Key Responsibilities: Azure Databricks Architecture & Design: Design, build, and optimize scalable, high-performance data pipelines and data lakes using Azure Databricks. Architect and implement end-to-end analytics solutions leveraging Databricks and other Azure services (e.g., Azure Data Lake, Azure SQL, Azure Blob Storage). Lead the design of cloud-based architectures using Azure Databricks for data processing, transformation, and reporting

Data Engineering & Integration: Design and implement ETL/ELT processes to ingest, process, and transform data across various sources (structured and unstructured). Collaborate with data scientists, analysts, and other stakeholders to understand business requirements and develop data solutions. Manage data integration workflows between Databricks and other platforms like Power BI, Azure SQL, Synapse, etc.

Optimization & Performance Tuning: Identify performance bottlenecks in Databricks environments and optimize clusters, queries, and code. Continuously improve and scale data pipelines to accommodate growing data volumes and business needs. Collaboration & Leadership: Mentor and guide junior engineers and team members in best practices related to Azure Databricks and data engineering.

Work closely with cross-functional teams (data science, analytics, business intelligence) to deliver integrated solutions. Provide technical leadership in cloud data architecture and data engineering. Monitoring & Security: Implement monitoring and alerting systems to ensure data pipelines and workflows are running smoothly.

Ensure compliance with data security and governance standards across the data ecosystem. Continuous Improvement & Innovation: Stay up to date with the latest advancements in Azure Databricks, cloud technologies, and data engineering trends. Continuously evaluate and introduce new technologies and methodologies to enhance the data platform.

Required Skills and Qualifications: Experience: Minimum 10 years of hands-on experience in data engineering and cloud-based data platforms. At least 5 years of experience working with Azure Databricks, building data pipelines, and performing data engineering tasks. Strong experience with Azure services such as Azure Data Lake, Azure Synapse Analytics, Azure Blob Storage, Azure SQL Database, etc.

Technical Skills: Proficiency in Spark, PySpark, Scala, or SQL for large-scale data processing. Expertise in Databricks notebooks, clusters, job scheduling, and libraries. Deep understanding of cloud-native data engineering practices and architecture on Azure.

Familiarity with data modeling, data lakes, and data warehouse concepts. Expertise in Azure services: Data Factory, Data Lake, Synapse Analytics, Event Hubs, Cosmos DB. Data Pipeline and ETL/ELT Development: Experience building and optimizing ETL/ELT workflows on Databricks.

Knowledge of data orchestration tools such as Azure Data Factory, Apache Airflow, or similar. Big Data Technologies: Proficiency in handling large-scale distributed data processing with tools like Apache Spark. Experience with technologies such as Kafka, Delta Lake, and Databricks Runtime.

Strong understanding of Delta Lake and Lakehouse architecture. Programming Languages: Strong programming skills in Python, Scala, or Java. Experience with SQL-based querying and optimizations.

Cloud & DevOps: Strong understanding of Azure cloud services and architecture. Familiarity with DevOps principles, CI/CD pipelines, and automation tools. Terraform required.

Knowledge of Git, Azure DevOps, or similar version control and deployment systems. Collaboration & Leadership: Excellent communication skills to work with both technical and non-technical stakeholders. Proven track record of mentoring and leading teams of data engineers.

Experience managing complex projects and working with cross-functional teams. Preferred Qualifications: Certifications: Microsoft Certified: Azure Data Engineer Associate or equivalent certification. Databricks Certified Associate Developer for Apache Spark.

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.