AI Data Engineer – Azure
Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Nov 11, 2026. Symphoni HR scores A on the Alion truth index.
This role centres on designing, building and operating scalable data pipelines and AI-enabled data platforms on Microsoft Azure. You will work with modern lakehouse and big data technologies to deliver reliable, well-modelled data for analytics and AI use cases. The position also involves applying agentic AI patterns and AI-powered development tools to accelerate engineering delivery. The role is based in Bengaluru with a hybrid working model.
Our client is a leading global professional services firm in the accounting and finance sector, serving organisations across multiple industries worldwide. They are known for large-scale digital transformation work and for investing heavily in data, analytics and artificial intelligence. The culture emphasises collaboration, continuous learning and delivering high-quality solutions to complex business challenges.
Responsibilities
- Design, develop and maintain end-to-end ETL and ELT data pipelines using Azure Databricks, Azure Data Factory and Apache Spark.
- Build and optimise data models (normalised and denormalised) across OLTP and OLAP environments to support analytics and reporting.
- Integrate data warehouses and BI tools, ensuring data quality, performance and reliability across the platform.
- Develop and manage data storage solutions using Azure Data Lake Storage, Azure Blob Storage and Azure Synapse Analytics.
- Ingest and process streaming and batch data using tools such as Kafka, PySpark and Hadoop where appropriate.
- Implement CI/CD practices using Azure DevOps and version control with Git within Agile delivery teams.
- Apply applied AI/ML frameworks and agentic AI patterns, including creating custom MCP skills and agents, to enhance data solutions.
- Leverage AI-powered development tools such as OpenAI Codex, Claude and Cursor to improve engineering productivity and code quality.
- Collaborate with architects, analysts and business stakeholders to translate requirements into robust technical solutions.
Requirements
- 4-7 years of hands-on experience in data engineering, including building production data pipelines.
- Strong proficiency in SQL and Python, with working knowledge of PySpark and Apache Spark.
- Practical experience with Databricks and Azure Databricks, Azure Data Factory, and core Azure data services.
- Solid understanding of data modelling (normalisation and denormalisation), data warehousing and OLTP/OLAP concepts.
- Experience with Git, Azure DevOps for CI/CD and Agile delivery methodologies.
- Familiarity with Kafka, Hadoop and big data ecosystems is desirable.
- Exposure to applied AI/ML frameworks, MCP (Model Context Protocol) and agentic AI patterns is highly valued.
- Strong problem-solving skills, clear communication and the ability to work effectively in a hybrid team environment.
Skills
SQL, Python, PySpark, Databricks, Azure Databricks, Azure Data Factory, Apache Spark, Azure Blob Storage, Azure Data Lake Storage, Data modeling (normalization and denormalization), Data warehousing and BI tools integration, Git, ETL pipeline development, Applied AI/ML frameworks, MCP (Model Context Protocol), Agentic AI patterns, AI-powered development tools (OpenAI Codex, Claude, Cursor), OLTP and OLAP environments, Kafka, Hadoop, Azure Synapse Analytics, Azure DevOps for CI/CD, Agile delivery methodologies, Creating custom MCP skills and agents