About this role
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.
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
• 10+ years experience with a strong Data Engineering background (ETL/ELT, distributed compute, production-grade pipelines). • 4+ years hands-on Databricks experience in architecture/technical leadership roles. • Strong experience in Apache Spark (PySpark/Scala), Delta Lake, pipeline design, and performance tuning. • 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
• 10+ years experience with a strong Data Engineering background (ETL/ELT, distributed compute, production-grade pipelines). • 4+ years hands-on Databricks experience in architecture/technical leadership roles. • Strong experience in Apache Spark (PySpark/Scala), Delta Lake, pipeline design, and performance tuning. • 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
• 10+ years experience with a strong Data Engineering background (ETL/ELT, distributed compute, production-grade pipelines). • 4+ years hands-on Databricks experience in architecture/technical leadership roles. • Strong experience in Apache Spark (PySpark/Scala), Delta Lake, pipeline design, and performance tuning. • 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