Lead Databricks Data Engineer ( MLOPs)

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.