Key Role & Responsibilities

• Define lakehouse architecture: medallion (bronze/silver/gold) patterns, batch/streaming designs, and multi-workspace strategies.

• Design and implement data pipelines using Spark, Delta Lake, and Databricks workflows (Jobs/Workflows, DLT where applicable).

• Establish governance and security using Unity Catalog, access controls, lineage, and data quality gates.

• Optimise performance: cluster policies, autoscaling, partitioning, file sizing, caching, Spark tuning, and job orchestration.

• Build CI/CD and release governance for notebooks, repos, jobs, and infrastructure-as-code.

• Integrate Databricks with enterprise ecosystem (cloud storage, event streaming, data warehouse, BI tools).

• Conduct solution workshops with customers; provide options and trade-offs; create phased implementation roadmaps aligned to business value.

• Mentor teams, enforce engineering standards, and ensure operational excellence (monitoring, incident response, SRE practices).

Must Have

• 12+ years experience with a strong Data Engineering background (ETL/ELT, distributed compute, production-grade pipelines).

• 5+ years hands-on Databricks experience in architecture/technical leadership roles.

• Strong experience in Apache Spark (PySpark/Scala), Delta Lake, pipeline design, and performance tuning.

• At least 1 Databricks Certification.

• Experience with data orchestration and DevOps practices (Git, CI/CD, testing frameworks).

• Experience designing secure data platforms (RBAC, secrets, network/security integration, compliance considerations).

• Strong customer-facing skills: requirements discovery, solution design, and stakeholder management.

Good to Have

• Streaming experience (Kafka/Event Hubs, Structured Streaming, CDC patterns).

• ML/AI enablement experience (MLflow, feature engineering, model lifecycle) as it relates to platform design.

• Cloud certifications or platform-specific certifications.

Education

• Bachelor’s/Master’s in Computer Science, Engineering, or related fields.

Key Skills: Databricks, Python, Spark, Data Architecture, Data Pipelines

Key Role & Responsibilities

• Define lakehouse architecture: medallion (bronze/silver/gold) patterns, batch/streaming designs, and multi-workspace strategies.

• Design and implement data pipelines using Spark, Delta Lake, and Databricks workflows (Jobs/Workflows, DLT where applicable).

• Establish governance and security using Unity Catalog, access controls, lineage, and data quality gates.

• Optimise performance: cluster policies, autoscaling, partitioning, file sizing, caching, Spark tuning, and job orchestration.

• Build CI/CD and release governance for notebooks, repos, jobs, and infrastructure-as-code.

• Integrate Databricks with enterprise ecosystem (cloud storage, event streaming, data warehouse, BI tools).

• Conduct solution workshops with customers; provide options and trade-offs; create phased implementation roadmaps aligned to business value.

• Mentor teams, enforce engineering standards, and ensure operational excellence (monitoring, incident response, SRE practices).

Must Have

• 12+ years experience with a strong Data Engineering background (ETL/ELT, distributed compute, production-grade pipelines).

• 5+ years hands-on Databricks experience in architecture/technical leadership roles.

• Strong experience in Apache Spark (PySpark/Scala), Delta Lake, pipeline design, and performance tuning.

• At least 1 Databricks Certification.

• Experience with data orchestration and DevOps practices (Git, CI/CD, testing frameworks).

• Experience designing secure data platforms (RBAC, secrets, network/security integration, compliance considerations).

• Strong customer-facing skills: requirements discovery, solution design, and stakeholder management.

Good to Have

• Streaming experience (Kafka/Event Hubs, Structured Streaming, CDC patterns).

• ML/AI enablement experience (MLflow, feature engineering, model lifecycle) as it relates to platform design.

• Cloud certifications or platform-specific certifications.

Education

• Bachelor’s/Master’s in Computer Science, Engineering, or related fields.

Key Skills: Databricks, Python, Spark, Data Architecture, Data Pipelines