Lead Databricks Data Engineer

ExlService Holdings, Inc.; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

ExlService Holdings, Inc. in New York, NY seeks a Lead Databricks Data Engineer with 10-12 years of experience to drive design, development, and modernization of enterprise data platforms using Databricks Lakehouse.

This hands-on leadership role demands architecture, mentoring, stakeholder collaboration, and delivering scalable cloud-native data solutions across Azure/AWS/GCP, with emphasis on governance, performance, and AI/ML readiness.

Qualifications

• 10-12 years of experience in Data Engineering, Big Data and Cloud Data Platforms.
• Experience leading enterprise-scale data platforms and modernization initiatives.
• Hands-on in building scalable cloud-native data ecosystems and governance frameworks.

Responsibilities

• Design and implement enterprise-scale data platforms using Databricks Lakehouse architecture.
• Architect and develop scalable ETL/ELT data pipelines for batch and real-time processing.
• Define enterprise data models, ingestion frameworks, and transformation strategies.
• Lead the implementation of Delta Lake, Unity Catalog, and data governance frameworks.
• Build and optimize Spark and PySpark solutions handling large-volume datasets.

Skills

Databricks Lakehouse Platform

Delta Lake

Unity Catalog

Structured Streaming

Databricks SQL

PySpark

Spark

Kafka

Data Governance

Education

Bachelor's Degree in Computer Science, Engineering, IT

Tools

Azure Databricks

AWS Glue

Databricks Workflows

Terraform

Jenkins

GitHub Actions

Job description

Job Description: Lead Databricks Data Engineer

Job Title: Lead Databricks Data Engineer
Experience: 10-12 Years
Employment Type: Full-Time

Job Summary

We are looking for a highly experienced Lead Databricks Data Engineer with 10-12 years of experience in Data Engineering, Big Data, and Cloud Data Platforms. The ideal candidate will lead the design, development, modernization, and optimization of enterprise-scale data platforms using Databricks Lakehouse architecture. This role requires strong technical leadership, solution architecture expertise, stakeholder management skills, and hands-on experience building scalable cloud-native data ecosystems.

The candidate will be responsible for defining data engineering standards, mentoring teams, driving modernization initiatives, and delivering high-performance data solutions that support analytics, AI/ML, and business intelligence initiatives.

Key Responsibilities Data Engineering & Architecture

• Design and implement enterprise-scale data platforms using Databricks Lakehouse architecture.
• Architect and develop scalable ETL/ELT data pipelines for batch and real-time processing.
• Define enterprise data models, ingestion frameworks, and transformation strategies.
• Lead the implementation of Delta Lake, Unity Catalog, and data governance frameworks.
• Build and optimize highly scalable Spark and PySpark solutions handling large-volume datasets.
• Drive cloud data platform modernization and migration initiatives.

Technical Leadership

• Serve as the technical lead for Databricks and Data Engineering programs.
• Establish engineering best practices, coding standards, and architecture guidelines.
• Conduct architecture reviews, code reviews, and solution assessments.
• Mentor and guide Data Engineers, Technical Leads, and junior team members.
• Collaborate with Enterprise Architects, Product Owners, Data Scientists, and Business Stakeholders.

Cloud & Platform Engineering

• Design cloud-native data solutions on Azure, AWS, or GCP.
• Implement secure, scalable, and resilient data architectures.
• Optimize costs, performance, reliability, and availability of data platforms.
• Lead CI/CD and Infrastructure-as-Code implementation for data workloads.
• Define and implement monitoring, observability, and operational excellence practices.

Governance & Data Quality

• Establish data governance, lineage, cataloging, and security controls.
• Ensure compliance with organizational and regulatory requirements.
• Implement data quality frameworks and monitoring solutions.
• Collaborate with security and compliance teams to enforce data protection standards.

Stakeholder & Delivery Management

• Partner with business leaders to understand strategic data requirements.
• Translate business requirements into scalable technical solutions.
• Drive delivery planning, risk management, and resource coordination.
• Present architecture decisions and technical recommendations to leadership teams.

Required Technical Skills Databricks

• Databricks Lakehouse Platform
• Databricks Workflows
• Delta Lake
• Unity Catalog
• Structured Streaming
• Databricks SQL
• Delta Live Tables (DLT)
• Databricks Asset Bundles

Big Data Technologies

• Apache Spark
• PySpark
• Spark SQL
• Kafka
• Real-Time Streaming Architectures
• Data Lake & Lakehouse Architectures

Cloud Platforms (Strong Experience in One or More) Azure

• Azure Databricks
• Azure Data Factory (ADF)
• ADLS Gen2
• Azure Synapse Analytics
• Azure Key Vault
• Event Hub

AWS

• S3
• EMR
• Glue
• Redshift
• Lambda
• IAM

GCP

• BigQuery
• Cloud Storage
• Dataflow
• Pub/Sub
• Composer

Programming

• Python
• SQL
• PySpark
• Scala (Preferred)
• Shell Scripting

DevOps & Automation

• Git
• Azure DevOps
• GitHub Actions
• Jenkins
• Terraform
• Docker
• Kubernetes

Data Warehousing

• Dimensional Modeling
• Star & Snowflake Schema
• Data Vault
• Master Data Management
• Data Governance Frameworks

Required Experience

• 12-15 years of overall IT experience.
• Minimum 6+ years of hands-on Databricks experience.
• 7+ years of experience in Spark/PySpark-based data engineering.
• Experience designing enterprise-scale Data Lake and Lakehouse solutions.
• Strong experience leading large data transformation and modernization programs.
• Experience handling structured, semi-structured, and unstructured data processing.
• Experience supporting analytics, AI/ML, and reporting ecosystems.
• Proven experience managing technical teams and driving architectural decisions.

Preferred Qualifications

• Bachelor's or Master's Degree in Computer Science, Engineering, Information Technology, or related field.
• Databricks Certified Data Engineer Professional.
• Azure Data Engineer Associate.
• AWS Data Analytics Specialty.
• Google Professional Data Engineer Certification.
• TOGAF or Cloud Architecture certifications are a plus.

Nice to Have

• MLOps and AI Engineering exposure.
• Snowflake and Databricks integration experience.
• Microsoft Fabric exposure.
• Data Mesh implementation experience.
• Data Governance tools such as Collibra, Alation, or Informatica.
• Expertise in Data Quality frameworks such as Great Expectations or Deequ.

Key Competencies

• Solution Architecture
• Technical Leadership
• Stakeholder Management
• Program Delivery
• Data Strategy & Governance
• Team Mentoring & Coaching
• Problem Solving & Decision Making
• Communication & Executive Presentation Skills
• Performance Optimization
• Innovation & Continuous Improvement

Technology Classification

Technology Tower: Data & AI
Practice Area: Data Engineering
Primary Skill: Databricks Lakehouse Engineering
Secondary Skills: Apache Spark, PySpark, Cloud Data Platforms (Azure/AWS/GCP), Data Architecture, Data Governance, MLOps.