Job Description
Databricks Engineer (Big Data Engineer) 3Job Location: Remote (Texas Residents only)
Job Type: Long-term Contract
Job Description
The Worker is responsible for developing, maintaining, and optimizing big data solutions using the Databricks Unified Analytics Platform.
This role supports data engineering, machine learning, and analytics initiatives within this organization that relies on large-scale data processing.
Duties include:
• Designing and developing scalable data pipelines
• Implementing ETL/ELT workflows
• Optimizing Spark jobs
• Integrating with Azure Data Factory
• Automating deployments
• Collaborating with cross-functional teams
• Ensuring data quality, governance, and security
• Building AI accelerators and reusable components to boost development teams’ productivity.
Required Skill & Qualification :
• Implement ETL/ELT workflows for both structured and unstructured data
• Collaborate with cross-functional teams including data scientists, analysts, and stakeholders
• Design and maintain data models, schemas, and database structures to support analytical and operational use cases
• Implement data validation and quality checks to ensure accuracy and consistency
• Contribute to data governance initiatives, including metadata management, data lineage, and data cataloging
• Implement data security measures, including encryption, access controls, and auditing; ensure compliance with regulations and best practices
• Working in agile, multicultural environments
• Automate deployments using CI/CD tools
• Evaluate and implement appropriate data storage solutions, including data lakes (Azure Data Lake Storage) and data warehouses
• Proficiency in Python and R programming languages
• Strong SQL querying and data manipulation skills
• Experience with Azure cloud platform
• Experience with DevOps, CI/CD pipelines, and version control systems
• Strong troubleshooting and debugging capabilities
• Design and develop scalable data pipelines using Apache Spark on Databricks
• Optimize Spark jobs for performance and cost efficiency
• Integrate Databricks solutions with cloud services (Azure Data Factory)
• Ensure data quality, governance, and security using Unity Catalog or Delta Lake
• Deep understanding of Apache Spark architecture, RDDs, DataFrames, and Spark SQL
• Hands-on experience with Databricks notebooks, clusters, jobs, and Delta Lake
• Build AI accelerators and reusable components to boost development teams’ productivity.
• Implement Gen AI / LLM application and utilities (Agentic AI, Harness / Prompt engineering, RAG)
• Implement AI application governance, observability and evaluation framework
• Design & Implement vector stores for knowledge bases and agent memory
• Integrate AI applications / utils / tools with enterprise traceability and SIEM tools
• Knowledge of ML libraries (MLflow, Scikit-learn, TensorFlow)