Databricks

W2 role and Onsite Key Responsibilities: • Build and maintain robust ETL/ELT pipelines using PySpark and Databricks notebooks • Design and implement scalable data solutions leveraging Delta Lake, Unity Catalog, and Lakehouse architecture • Optimize Spark jobs for performance and cost efficiency • Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data products • Integrate Databricks with cloud platforms (AWS, Azure, or GCP) and external data sources • Ensure data quality, governance, and security compliance across pipelines • Participate in code reviews, architecture discussions, and platform enhancements Required Skills: • 5+ years of experience in data engineering, with 2+ years on Databricks • Proficiency in PySpark, SQL, and Databricks notebooks • Strong understanding of Delta Lake, data lakehouse principles, and distributed computing • Experience with cloud platforms (AWS, Azure, or GCP) and associated services (e.g., S3, ADLS, BigQuery) • Familiarity with CI/CD pipelines, Git, and DevOps practices • Knowledge of data governance tools (Unity Catalog, Purview, etc.) • Excellent problem-solving and communication skills Preferred Qualifications: • Databricks Certified Data Engineer Associate or Professional • Experience with MLflow, Auto Loader, and structured streaming • Exposure to Airflow, dbt, or other orchestration tools Education: • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field Let me know if you'd like this adapted for a specific cloud (AWS, Azure, GCP), formatted for outreach, or paired with hashtags.