EY - GDS Consulting - AI and DATA - Kafka Engineer - Senior

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EY Hiring Requisition

Senior Consultant - Data Engineering & Data Architecture

Experience: 5-7 Years

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.

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