About the Role:

We are looking for a highly accomplished Databricks Practice Lead to establish and grow our Databricks competency. This individual will define the technical vision, build a world-class Databricks practice, drive customer success, lead enterprise data modernization initiatives, support strategic sales pursuits, mentor architects, and create reusable accelerators.

Key Responsibilities:

1. Practice Leadership:

- Build and own the Databricks Practice strategy and roadmap.

- Define architecture standards, coding standards, governance, and best practices.

- Build reusable frameworks, accelerators, templates, and automation assets.

- Hire, mentor, and develop Databricks Architects and Senior Data Engineers.

- Drive competency growth and certification initiatives.

2. Technical Leadership:

- Lead enterprise implementations using Databricks Lakehouse, Delta Lake, Unity Catalog, Delta Live Tables, Databricks SQL, Workflows, MLflow, Photon, Serverless Compute, and Lakehouse Federation.

- Design scalable, high-performance enterprise data platforms.

3. Data Modernization:

- Lead migration from Oracle, Teradata, Hadoop, Informatica, ODI, SSIS, Azure Synapse and legacy EDW platforms to Databricks.

- Deliver modernization programs for HR, ERP, Finance, Supply Chain, Procurement and Customer Analytics.

4. Architecture Responsibilities:

- Design Lakehouse, Data Warehouse and Data Lake solutions.

- Build metadata-driven ingestion frameworks using APIs, files, ERP, CRM, Oracle Fusion, SAP, Salesforce and Workday.

- Implement CDC, data quality, governance, lineage and security.

5. Cloud Expertise:

- Azure: Databricks, ADF, ADLS Gen2, Synapse, Azure SQL, Key Vault.

- AWS: EMR, Glue, S3, Athena, Redshift, MWAA.

- GCP: BigQuery, Cloud Storage, Composer, Compute Engine.

6. Modern Data Stack:

- PySpark, Spark SQL, Python, SQL

- Snowflake, DBT, Microsoft Fabric

- Airflow (MWAA, Composer, Astronomer)

7. Performance Optimization:

- Spark optimization, Delta optimization, partitioning, Z-Ordering, incremental processing, workload and cost optimization.

8. Delivery & Client Leadership:

- Lead teams of 1550 engineers.

- Conduct architecture reviews and mentor technical teams.

- Engage with CIOs, Enterprise Architects and business stakeholders.

- Support RFPs, solutioning, PoCs and executive presentations.

9. DevOps & Governance:

- Git, Azure DevOps, GitHub, Bitbucket, CI/CD, Terraform (preferred).

- Unity Catalog, RBAC, Data Governance, Data Quality and Security.

Required Skills:

- 14+ years in Data Engineering with 6+ years on Databricks.

- Strong PySpark, SQL, Python and Spark optimization.

- Experience across Azure, AWS and GCP.

- Hands-on expertise in Snowflake, Microsoft Fabric, DBT and Airflow.

- Experience leading enterprise-scale modernization programs.

Preferred Certifications:

- Databricks Certified Data Engineer Professional

- Databricks Certified Solution Architect

- Azure Solutions Architect Expert

- Azure Data Engineer Associate

- SnowPro Advanced

- Microsoft Fabric Analytics Engineer

Ideal Candidate:

- Can build and scale a Databricks Practice.

- Acts as a trusted advisor for enterprise customers.

- Balances deep technical expertise with leadership and pre-sales capabilities.

- Possesses experience comparable to a senior enterprise architect with broad multi-cloud, Databricks, Snowflake, Fabric, DBT and Airflow expertise.