Databricks Engineer/ Architect- EST Timezone

About the Role: We are looking for experienced Databricks Engineers / Architects to join a fast-moving data transformation initiative. The client is already live on Databricks with production pipelines and reporting in place, but the platform requires stabilization, optimization, and scaling . This is an opportunity to work in a high-impact environment , helping transform an evolving data platform into a reliable, production-grade system delivering consistent business value. Key Responsibilities: Design, build, and stabilize Databricks-based data pipelines Perform pipeline optimization and production hardening Develop and support reporting and data visualization solutions Implement best practices for performance, scalability, and cost optimization Work on end-to-end data workflows (ingestion → transformation → reporting) Identify and resolve performance bottlenecks and system inefficiencies Bring structure and consistency to an evolving Databricks environment Collaborate with cross-functional teams to improve platform reliability and ROI Required Skills & Experience: Strong hands-on experience with Databricks (must-have) Experience in data pipeline development (ETL/ELT) Solid understanding of data engineering and data architecture principles Experience working with large-scale data processing systems Exposure to reporting/BI tools (Power BI, Tableau, or similar) Ability to work in fast-paced, evolving environments with minimal handholding Preferred / Good-to-Have Skills: Familiarity with Apache Spark (PySpark / Scala) Experience with cloud platforms (AWS / Azure / GCP) Knowledge of performance tuning and cost optimization in Databricks Exposure to data governance, security, and best practices Prior experience in stabilizing or scaling data platforms Ideal Candidate Profile: Can quickly ramp up and contribute from day one Strong problem-solver with experience handling unstable or partially built systems Comfortable working in a “build while running” environment Balances technical expertise with delivery focus What Success Looks Like: Stabilized and optimized Databricks pipelines Improved platform reliability and performance Scalable and production-ready data workflows Consistent and measurable business value delivery Originally posted on Himalayas