Senior Azure AI Data Engineer

Azure Data Engineer responsible for building scalable data platforms and AI solutions at EY. Collaborating with engineers and architects to deliver impactful data and AI projects.

Posted 7/19/2026full-timeKolkata • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable data pipelines and AI solutions using Azure services and Databricks. Proficient in data engineering best practices, including ETL/ELT processes, data modeling, and integration with AI frameworks.

Highest-signal resume keywords

Data EngineeringAzure Data ServicesDatabricks (PySpark)ETL/ELT Pipeline DevelopmentAI Frameworks (Azure OpenAI, LangChain)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData ModelingMetadata ManagementEnterprise Data WarehousingNoSQL DatabasesPerformance OptimizationScalabilityReliabilityAPI IntegrationAdvanced Data Modeling Techniques

Soft Skills

Strong Communication SkillsProblem Solving

Tools & Technologies

Azure SynapseMicrosoft FabricCI/CD PracticesVersion ControlTesting and Deployment

Certifications & Qualifications

Bachelor’s Degree in Computer Science

Industry Keywords

AI SolutionsData Integration PatternsRAG SolutionsIntelligent Data DiscoveryData Ecosystems

Tech Stack

Tools & technologies

AzureETLNoSQLPySpark

About the role

Key responsibilities & impact

Actively contribute within a team of Engineers and Architects to design, prototype, and deliver scalable data and AI solutions, including proof of concepts (POCs) and production deployments.
Design and build scalable data pipelines and processing frameworks using Azure services, Databricks, and PySpark across large-scale enterprise environments.
Collaborate with business and technical stakeholders to translate requirements into robust data architectures and AI-enabled solution designs.
Develop and operationalize AI-enabled data use cases, including: Integration of enterprise data with LLM-based applications, Implementation of Retrieval Augmented Generation (RAG) solutions, Enabling intelligent data discovery and consumption using AI tools.
Implement end-to-end data solutions adhering to best practices in data engineering, including performance optimization, scalability, and reliability.
Work with modern AI and data ecosystems (e.g., Azure OpenAI, LangChain) to enable intelligent data workflows integrated with enterprise platforms.
Contribute to CI/CD pipelines, automation, and deployment processes for both data pipelines and AI workflows.

Requirements

What you’ll need

4–7 years of hands-on experience in data engineering
3+ years of experience with Azure data services and Databricks (PySpark)
Strong experience in building ETL/ELT pipelines and lakehouse architectures on Azure
Solid understanding of data modelling, data integration patterns, and metadata management
Experience with enterprise data warehousing solutions (Azure Synapse, Databricks, or Microsoft Fabric)
Working knowledge of LLM-based applications, embeddings, and RAG patterns
Hands-on experience with AI frameworks such as Azure OpenAI, LangChain, or similar
Experience in integrating systems using APIs, ETL tools, and modern data platforms
Strong experience with CI/CD practices, version control, testing, and deployment
Ability to solve complex data challenges at scale
Strong communication skills with ability to articulate technical and AI concepts clearly
Bachelor’s degree in Computer Science or related field
Experience with NoSQL databases and advanced data modelling techniques.

Benefits

Comp & perks

Support, coaching and feedback from some of the most engaging colleagues around
Opportunities to develop new skills and progress your career
The freedom and flexibility to handle your role in a way that’s right for you