As a Lead Databricks Engineer, you will play a critical role in delivering large-scale data transformation programs. You will lead the design and implementation of modern Lakehouse platforms on Databricks, working closely with client stakeholders, architects, and cross-functional teams. This is a client-facing leadership role requiring robust consulting capabilities, technical depth, and delivery ownership.

The Lead Databricks Engineer is responsible for designing and implementing scalable data platforms using Databricks, Apache Spark, and Delta Lake, including building robust batch and real-time data pipelines and developing ingestion frameworks for both structured and unstructured data while optimising performance, cost, and reliability. The role involves leading and mentoring a team of data engineers, driving best practices across coding, CI/CD, and DevOps, and collaborating with cross-functional stakeholders to translate business needs into effective data solutions, while owning end-to-end project delivery. Additionally, the engineer will implement data solutions across cloud platforms—primarily Azure, but also AWS or GCP—integrating with services such as Azure Data Factory, AWS Glue, and contemporary data warehouses like Snowflake or Synapse, while managing infrastructure through Infrastructure-as-Code tools. A strong focus is placed on data governance, including implementing quality checks, monitoring and observability frameworks,



ensuring compliance with security and privacy standards, and managing metadata and lineage using tools like Unity Catalog. The role also supports advanced analytics by enabling data science workflows, building ML-ready datasets using Databricks capabilities, and supporting real-time analytics through technologies such as Structured Streaming and Delta Live Tables.

- Strong experience in data engineering (enterprise-scale environments preferred).
- Deep expertise in Databricks, Apache Spark (PySpark preferred), and Delta Lake.
- Proven experience designing and implementing Lakehouse architectures (Medallion model).
- Advanced proficiency in SQL and Python.
- Solid experience with cloud platforms – Azure (ADLS, ADF, Synapse) or AWS (S3, Glue, Redshift).
- Experience building batch and real-time/streaming data pipelines.
- Hands-on experience with data orchestration tools (ADF, Airflow, Databricks Workflows).
- Experience implementing CI/CD pipelines and DevOps practices (Azure DevOps, GitHub, or similar).
- Working knowledge of Infrastructure-as-Code (Terraform or similar tools).
- Strong understanding of data modelling and data warehousing concepts.
- Experience working in client-facing consulting or system integrator environments.
- Proven ability to lead and mentor engineering teams, including onshore/offshore models.
- Strong stakeholder management, communication, and presentation skills.

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📌 Databricks Solution Lead - NCS Australia (South Australia)
🏢 OpenTalent
📍 South Australia