About the role
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EY Hiring Requisition
Manager - Data Engineering & Data Architecture
Experience: 10-12 Years
Service Line: Technology Consulting - Data & Analytics
Job Summary
EY is seeking a highly skilled Data Engineering and Data Architecture professional with strong expertise in Databricks, Kafka, AWS, and modern Data Lakehouse architecture.
The ideal candidate will have hands-on experience designing and implementing scalable data platforms, building enterprise-grade data pipelines, and delivering analytics-ready data products.
The candidate should possess deep technical knowledge across the data engineering lifecycle, from ingestion and transformation to modelling and consumption, while driving delivery through engineering best practices, automation, and stakeholder collaboration.
Key Responsibilities
Design and implement modern Data Lakehouse platforms using Databricks on AWS
Build ETL/ELT pipelines
Implement Kafka streaming solutions
Design Fact and Dimension models
Implement CI/CD using Azure DevOps and GitHub
Provide technical leadership
Data Architecture & Platform Engineering
Design and implement modern cloud-based Data Lakehouse platforms using Databricks on AWS.
Define and implement scalable Raw, Silver, and Gold layer architectures.
Architect end-to-end data solutions supporting reporting, BI, advanced analytics, and AI use cases.
Establish data platform standards, governance controls, and engineering best practices.
Data Engineering & ETL
Design, develop, and optimize large-scale ETL/ELT pipelines.
Build high-performance Spark-based data processing solutions.
Develop reusable and metadata-driven ingestion and transformation frameworks.
Ensure data quality, reliability, scalability, and operational efficiency across data pipelines.
Implement data validation, reconciliation, and monitoring mechanisms.
Handling semi-structured data formats- json, xml
Streaming & Real-Time Data Processing
Design and implement real-time data ingestion frameworks using Kafka.
Build scalable streaming solutions integrating Kafka with Databricks and AWS services.
Optimize streaming workloads for performance and resilience.
Data Modeling
Design conceptual, logical, and physical data models.
Create dimensional models, Fact tables, and Dimension tables for enterprise reporting and analytics.
Implement data models aligned with business and analytical requirements.
DevOps & Engineering Excellence
Implement CI/CD pipelines for data engineering workloads.
Leverage Azure DevOps, GitHub, Databricks Asset Bundles (DAB), and automation frameworks.
Promote version control, code reviews, testing, and release management practices.
Stakeholder & Team Management
Collaborate with business stakeholders, architects, and delivery teams to translate requirements into scalable solutions.
Provide technical leadership and mentoring to engineering teams.
Contribute to solution design workshops, estimations, and architecture reviews.
Required Skills
Core Technical Skills
Databricks (Expert Level or Databricks Champion)
Apache Spark (PySpark / Spark SQL)
Delta Lake
Kafka Streaming
Semi-structured data processing like json, xmls
AWS Cloud Services
ETL/ELT Design and Development
Data Lakehouse Architecture
Data Modeling and Dimensional Modeling
Data Warehousing Concepts
SQL and Performance Optimization
Real-Time Data Processing
Data Pipeline Orchestration
DevOps & Collaboration
GitHub
Azure DevOps
CI/CD Implementation
Databricks Asset Bundles (DAB)
Agile Delivery Methodology
Other relevant skills
Strong problem-solving and analytical skills.
Excellent communication and stakeholder management capabilities.
Ability to work in a global delivery environment.
Strong ownership mindset with focus on quality and client value.
Demonstrated leadership and mentoring capabilities.
Preferred Experience
Experience designing enterprise-scale Data Lakehouse solutions.
Hands-on implementation of Raw, Silver, and Gold architectural patterns.
Experience building metadata-driven ingestion and transformation frameworks.
Exposure to Insurance, Banking, or Financial Services domain data platforms.
Experience supporting analytics, reporting, and AI/ML workloads on Databricks.
Experience leading distributed teams and managing technical delivery.
Certifications (Preferred)
One or more of the following certifications are highly desirable:
Databricks Certified Data Engineer Associate/Professional
Databricks Certified Professional Certifications
Databricks Generative AI Engineer Associate
Databricks Lakeflow Certification
Databricks Asset Bundles (DAB) Certification
Educational Qualification
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
EY | Building a better working world
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