Sr Data Engineer

InfoVision Inc.; Maharashtra; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

InfoVision Inc. seeks a Senior Data Engineer in Pune with 6–8 years of hands-on experience designing, building, and maintaining scalable data pipelines using Azure Data Factory, Databricks, and Synapse.

The role emphasizes SQL proficiency and REST API data ingestion for enterprise data solutions. Experience with Claude AI for intelligent data workflows and strong RDBMS skills (MySQL, Oracle, MSSQL, PostgreSQL) is highly valued to deliver reliable data delivery and governance across analytics

Qualifications

• 6–8 years of hands-on data engineering experience in scalable pipelines.
• Strong SQL expertise and hands-on Azure cloud services knowledge.
• Experience building ETL/ELT pipelines and handling large datasets.

Responsibilities

• Design and implement end-to-end ETL/ELT pipelines using Azure Data Factory and Databricks.
• Write optimized SQL across MySQL, Oracle, MSSQL, and PostgreSQL.
• Develop REST API integrations to ingest data from external platforms and internal services.
• Leverage Claude AI to automate data workflows and query generation.
• Architect and manage Azure-based data infrastructure including Synapse and Data Lake Storage.
• Collaborate with analysts and BI teams to model data for reporting.
• Monitor and optimize pipelines for performance and data quality.
• Contribute to engineering standards and version control in Agile delivery.

Skills

Azure Data Factory

Azure Databricks

Azure Synapse

Blob Storage

SQL

REST APIs

Claude AI

RDBMS

Data Warehousing

Job description

Job Description

• We are seeking a Senior Data Engineer with 6-8 years of hands‑on experience in designing, building, and maintaining scalable data pipelines and cloud‑based data solutions in Pune.
• The ideal candidate must have strong expertise in SQL and Azure cloud services including Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure Blob Storage to support enterprise‑level data engineering initiatives.
• Proficiency in ETL/ELT pipeline development is essential, with demonstrated ability to extract, transform, and load large volumes of structured and unstructured data from multiple heterogeneous sources.
• Experience integrating and consuming REST APIs to ingest data from third‑party platforms and internal microservices is a core requirement for this role.
• Hands‑on experience with Claude AI or similar LLM‑based tools for data processing, automated query generation, or intelligent data transformation workflows will be considered a strong advantage.
• The candidate must be well‑versed in working with RDBMS platforms such as MySQL, Oracle, MSSQL, and PostgreSQL for data modeling, query optimization, and relational schema design.
• Strong understanding of data warehousing concepts, dimensional modeling, data governance, and data quality frameworks is required to ensure reliable and consistent data delivery.
• The role demands excellent problem‑solving skills, ability to collaborate with cross‑functional teams including data scientists, analysts, and business stakeholders to translate requirements into robust data solutions.

Roles & Responsibilities

Job Roles And Responsibilities

• Design and implement end‑to‑end ETL/ELT pipelines using Azure Data Factory and Azure Databricks to process and move data across various source systems and data lakes.
• Write optimized and complex SQL queries for data extraction, transformation, aggregation, and reporting across RDBMS platforms including MySQL, Oracle, MSSQL, and PostgreSQL.
• Develop and maintain REST API integrations to pull real‑time and batch data from external SaaS platforms, internal services, and third‑party data providers.
• Leverage Claude AI capabilities to assist in building intelligent data workflows, automating repetitive data transformation logic, and improving query generation efficiency.
• Architect and manage Azure‑based data infrastructure including Azure Synapse, Azure Data Lake Storage, and Azure SQL Database ensuring high availability, scalability, and performance.
• Collaborate with data analysts and business intelligence teams to define data models, build semantic layers, and ensure accurate and timely delivery of data for reporting and analytics.
• Monitor, troubleshoot, and optimize existing data pipelines for performance bottlenecks, data quality issues, and pipeline failures with minimal downtime.
• Contribute to establishing data engineering best practices, coding standards, documentation, and version control processes using tools such as Git within an agile delivery environment.