- Technical Leadership : Provide technical leadership, guide, and mentor data engineering teams in designing, developing, and implementing robust and scalable Databricks solutions.

- Architecture & Design : Develop and implement scalable data pipelines and architectures using the Databricks Lakehouse platform, ensuring data quality, consistency, and accessibility.

- Data Engineering : Lead the ingestion, transformation, and processing of both batch and streaming data from diverse sources into the Databricks environment.

- Performance Optimization : Analyze and ensure efficient resource utilization within Databricks clusters and troubleshoot performance bottlenecks in data pipelines and queries.

- Security & Compliance : Implement and enforce best practices for data governance, access control, data lineage, and compliance within the Databricks ecosystem, including a strong focus on data security.

- Collaboration : Work closely with data engineers, data analysts, data scientists, and business stakeholders to understand data requirements and deliver effective data solutions.

- Cloud Integration : Manage and optimize Databricks environments on various cloud platforms, including AWS and Azure (and potentially GCP), leveraging their respective data services.

- Monitoring & Automation : Set up comprehensive monitoring tools and automate data workflows and operational tasks for increased efficiency and reliability.

- Develop and optimize complex SQL queries for data extraction, transformation, and loading.

Required Skills & Qualifications :

- 8+ years of hands-on experience in Databricks, Apache Spark, and big data processing.

- Proficiency in Python, Scala, or SQL for data engineering and development within Databricks.

- Strong knowledge of Delta Lake, Unity Catalog, and MLflow.

- Extensive experience with ETL (Extract, Transform, Load) processes and ELT (Extract, Load, Transform) methodologies.

- Proven experience working with cloud platforms, specifically AWS and Azure.

- Excellent problem-solving, analytical, and leadership skills.

- Strong understanding of data warehousing and data lakehouse concepts.

- Experience with version control tools (Git).

- Good communication skills (written and verbal) and ability to articulate complex technical concepts.

- Bachelors or Masters degree in Computer Science, Engineering, or a related quantitative field- or equivalent practical experience