Role : Lead Databricks Data Engineer ( MLOPs) Years: 8+ Years Notice period : currently serving notice period ( immediate joiner ) Key Points while sourcing : Make sure they can work IC and Having good communication to interact with Client Mandatory Skills : Databricks, Python, Pyspark, CI/CD, MLOps MLOps: how to Integration works, Connections, ML works
As a Lead Databricks Engineer at company supporting Databricks, you will be embedded within customer engagements as a trusted technical advisor. You will apply deep, hands-on Databricks development experience and a strong command of distributed computing on Spark — including Spark runtime internals — to design, build, and optimize production-grade data solutions. You will bring current knowledge across the breadth of the Databricks product and platform, establish CI/CD and MLOps best practices, and work fluently across cloud ecosystems to ensure scalable, performant, and reliable delivery for customers. KEY RESPONSIBILITIES
Serve as a lead engineer and trusted technical advisor on customer Databricks engagements, from design through production deployment
Deliver hands-on development on Databricks across multiple customer projects
Design and optimize distributed-computing workloads on Spark, applying knowledge of Spark runtime internals
Implement CI/CD for production deployments
Operationalize MLOps workflows to support machine-learning use cases
Advise on and apply the breadth of Databricks product and platform features
Tune workloads for performance, scalability, and cost efficiency
Work across two or more cloud ecosystems (AWS, Azure, GCP), applying deep expertise in at least one
Guide customers through best-practice lakehouse implementation and production readiness
Collaborate with customer stakeholders and onshore leads overlapping PST hours to ensure timely, high-quality delivery REQUIRED QUALIFICATIONS & SKILLS
8–12 years of experience in data engineering, data platforms, and analytics, including substantial consulting experience.
Completed the Databricks Data Engineering Professional certification and the required Databricks classes.
Minimum 6–8+ projects delivered with hands-on development experience on Databricks.
Working knowledge of two or more common cloud ecosystems (AWS, Azure, GCP), with deep expertise in at least one.
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