Senior Data Engineer

Experteer Overview In this Senior Data Engineer role, you will shape and deliver end-to-end data solutions for client engagements within Deloitte’s Project Delivery Model. You’ll collaborate with engagement managers and cross-functional teams to design scalable data pipelines and governance. Expect to tackle large-scale transformations, optimize performance, and mentor junior engineers as you drive data platform modernization for complex environments. This opportunity lets you impact real-world outcomes while working in a collaborative, innovation-focused culture. Compensation / Benefits * Design, develop and optimize ETL/ELT pipelines with Azure Data Factory and Databricks * Write and tune PySpark / Spark SQL notebooks for large-scale transformations * Architect end-to-end data solutions across dev, UAT, prod using Unity Catalog * Lead design discussions with client architects and cross-functional partners * Collaborate on data contracts and schema governance * Drive high-volume data pipeline design and optimization * Define data engineering standards and tuning (AQE, partitioning, joins) * Configure Databricks clusters, autoscaling, and cost optimization * Perform root cause analysis on production incidents and implement durable fixes * Mentor junior/mid-level engineers via code reviews and pair programming * Evaluate new technologies and recommend adoption (DABs, DLT, Auto Loader, Serverless Compute, Event Hubs) Tasks * Python, PySpark, Spark SQL, SQL Server * Azure (ADF, ADLS Gen2, Key Vault, Azure Monitor) * Databricks (Delta Lake, Unity Catalog, Workflows) * Apache Airflow * Git / Azure DevOps * Deep Spark internals (DAG optimization, spill analysis, skew handling) * Delta Lake advanced features (time travel, deletion vectors, predictive I/O) * Unity Catalog governance (row/column security, external locations, system tables) * IaC - Terraform, Azure ARM templates * Bachelor’s degree or equivalent experience * Ability to travel 10% Key requirements *

• pipeline design and optimization * Define data engineering standards and tuning (AQE, partitioning, joins) * Configure Databricks clusters, autoscaling, and cost optimization * Perform root cause analysis on production incidents and implement durable fixes * Mentor junior/mid-level engineers via code reviews and pair programming * Evaluate new technologies and recommend adoption (DABs, DLT, Auto Loader, Serverless Compute, Event Hubs) Tasks * Python, PySpark, Spark SQL, SQL Server * Azure (ADF, ADLS Gen2, Key Vault, Azure Monitor) * Databricks (Delta Lake, Unity Catalog, Workflows) * Apache Airflow * Git / Azure DevOps * Deep Spark internals (DAG optimization, spill analysis, skew handling) * Delta Lake advanced features (time travel, deletion vectors, predictive I/O) * Unity Catalog governance (row/column security, external locations, system tables) * IaC - Terraform, Azure ARM templates * Bachelor’s degree or equivalent experience * Ability to travel 10% Key requirements *