Senior Data Engineer - AI and Data Science Engineer III

Senior Data Engineer

About the Role

Are you an experienced Data Engineer who has led the development of world-class software or digital products? Are you looking for an opportunity to expand your scope and lead products used by some of the biggest and most influential businesses around the world? If the answer to these questions is yes, we would like to talk with you.

ConvergeCONSUMER™ is Deloitte’s product-driven business that combines differentiated consumer insights with robust prediction and optimization capabilities to help consumer-focused businesses improve decision-making and deliver personalized experiences to drive growth, consumer loyalty, and profitability. We operate with the speed and agility of a startup within the world’s largest professional services firm.

We drive value for our clients by optimizing decisions and actions at a level of granularity only made possible with today’s data and technology. ConvergeCONSUMER’s suite of solutions helps consumer-facing businesses prosper in a world being re-shaped by convergence, where the lines between industries, competitors and collaborators, consumers and suppliers, and even human and artificial intelligence have blurred. Our approach for addressing this complexity is simple: we combine Deloitte’s next-generation technology platforms with decades of experience in the consumer sector and a deep understanding of its unique issues and challenges to drive profitability and future growth for our clients.

The ConvergeCONSUMER™ engineering team operates as a product organization within Deloitte, building scalable identity and decisioning platforms. We focus on engineering rigor, platform scalability, and reusable system design to power enterprise-grade data products across industries.

Key Responsibilities

• Design and architect scalable, reusable ETL and data processing frameworks supporting product use cases

• Build and maintain robust batch and streaming pipelines using Spark (PySpark / Spark SQL)

• Develop and optimize solutions across Snowflake and Databricks environments

• Design end-to-end data systems including ingestion, transformation, storage, and serving layers

• Build cloud-agnostic data solutions deployable across AWS, Azure, and GCP

• Establish and maintain CI/CD pipelines for automated build, test, and deployment of data workflows

• Containerize services using Docker and manage deployments via Kubernetes

• Implement performance tuning strategies for large-scale distributed processing workloads

• Develop advanced SQL- and Python-based transformation logic aligned to product requirements

• Design data models and storage strategies optimized for analytics and downstream applications

• Implement data validation, quality controls, monitoring, and observability frameworks

• Partner with product managers and data scientists to translate business requirements into scalable technical designs

• Contribute to platform architecture decisions and establish engineering best practices

• Operate within Agile sprints and deliver production-grade product features

Reporting Relationships

Reports to the Engineering Tech Lead / AVP – Product Engineering / AVP – Data Science and works as a core member of the product team alongside Product Management and UX. Partners closely with US-based product owners and portfolio leaders to translate client needs into scalable technical solutions, while collaborating with pFDE, GTM, account, and Deloitte Consulting teams across India and the US.

Qualifications

Required

• 8+ years of hands-on data engineering experience in product or platform environments

• Strong expertise in designing and implementing ETL frameworks and scalable data systems

• Advanced experience with Snowflake and Databricks

• Strong proficiency in Apache Spark (PySpark / Spark SQL)

• Advanced SQL skills with demonstrated performance tuning experience

• Strong Python programming skills for production-grade systems

• Experience designing systems deployable across AWS, Azure, and GCP

• Experience building and maintaining CI/CD pipelines for data products

• Experience with Docker and Kubernetes for containerized deployments

• Strong understanding of distributed systems architecture and large-scale data processing

• Experience with Git, branching strategies, and Agile product development

Preferred

• Experience designing cross-cloud data platforms

• Experience with Delta Lake / Lakehouse architectures

• Experience with orchestration tools such as Airflow or Databricks Workflows

• Infrastructure-as-Code experience (Terraform, ARM, CloudFormation)

• Experience building identity resolution, entity matching, or other join-heavy systems

• Experience working in product-led engineering teams

Core Competencies

• Distributed Systems & Platform Architecture

• Cross-Cloud Engineering (AWS, Azure, GCP)

• DevOps & CI/CD Discipline (Docker, Kubernetes, Automated Pipelines)

• End-to-End Ownership from Architecture through Production

• Agile Product Engineering Mindset

• Technical Leadership & Best-Practice Setting

Why This Role Matters

Great products are built by engineers who stay close to the user and take pride in the craft. This role builds a product for the US market from the ground up in India—turning real client problems into scalable product experiences, owning solutions from architecture through production, and iterating based on how clients actually use them. It’s where strong product engineers do their best hands-on work and grow into technical leaders who shape products and platforms next.