Practice Head - Data Engineering

Practice Head - Databricks

We are seeking an experienced Databricks Data Engineering Practice Lead to drive our Databricks service offerings and lead customer engagements. In this key role, you will be responsible for building and growing our Databricks practice, overseeing customer projects, and ensuring the successful delivery of data engineering solutions using Databricks technologies. You will serve as the technical authority on Databricks within our organization and play a crucial role in expanding our capabilities and market presence in this domain.

Responsibilities:

- Lead and grow the Databricks Data Engineering practice, developing service offerings, methodologies, and best practices tailored to customer needs.

- Oversee multiple customer projects, providing technical leadership and ensuring high-quality delivery of Databricks-based data engineering solutions.

- Architect complex data solutions leveraging Databricks technologies, including Delta Lake, Apache Spark, and MLflow, addressing customers' data processing, analytics, and machine learning needs.

- Collaborate with sales teams to develop proposals, scope projects, and articulate the value proposition of Databricks solutions to potential clients.

- Mentor and guide data engineers within the practice, fostering their professional growth and enhancing the team's overall Databricks expertise.

- Stay at the forefront of Databricks technologies, features, and best practices, ensuring our practice remains cutting-edge and competitive.

- Develop and maintain strong relationships with Databricks, participating in partner programs and leveraging resources to enhance our service capabilities.

- Create training programs to upskill internal teams on Databricks technologies and best practices.

- Work closely with other practice leads to identify cross-practice opportunities and deliver integrated solutions to customers.

Qualifications:

- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.

- 10+ years of experience in data engineering, with at least 3-5 years of hands-on experience working with Databricks technologies.

- Proven expertise in designing and implementing large-scale data processing solutions using Databricks, Delta Lake, and Apache Spark.

- Strong understanding of cloud platforms (AWS, Azure, GCP) and their integration with Databricks.

- Proficiency in languages commonly used in Databricks environments, such as Python, Scala, and SQL.

- Experience with data lakehouse architectures, ETL/ELT processes, and data modeling techniques.

- Familiarity with machine learning workflows and MLOps practices, particularly using MLflow.