Top Data Engineer Jobs in Hyderabad

Build and support Product Master Data Management applications and integrations. Develop API layers with Spring Reactive, data pipelines, and deploy apps using Git and Kubernetes. Support agentic AI/LLM solutions, document system integrations, and collaborate across teams.

The Data Engineer will design and optimize data pipelines for the enterprise data warehouse, ensuring data quality, performance, and reliability while collaborating with stakeholders to meet data requirements.

Design and optimize Snowflake data solutions for a business rules modernization initiative. Responsibilities include refactoring SQL stored procedures, optimizing queries, designing Rules Catalog and Audit Repository databases, developing data pipelines, supporting integrations with Drools and Camunda, ensuring data integrity and scalability, and collaborating with architects, analysts, and application teams. The role also provides technical guidance and mentorship to junior engineers.

Build and maintain Python- and SQL-based data pipelines, perform ingestion, transformation, validation, and support analytics datasets and reporting. Use AI-assisted tools for development, debugging, optimization, and documentation. Collaborate on Fabric and lakehouse platforms while contributing to reusable data frameworks, automation, and business KPI reporting.

Develop backend data engineering solutions using AWS, Golang, and Python. Build and maintain ETL/ELT pipelines with Snowflake, AWS Glue, PySpark, and SQL. Support event-driven systems using Kafka and RabbitMQ, and implement CI/CD workflows using tools such as GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.

Design, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.

Designs, develops, and maintains scalable Azure data platforms, including ETL/ELT pipelines, data lakes, warehouses, and medallion architectures. Integrates structured and unstructured data from diverse sources, optimizes SQL and Spark processing, and ensures data quality, governance, security, and compliance. Monitors production workflows, troubleshoots performance issues, and implements CI/CD and infrastructure automation while collaborating with analysts, DevOps teams, and business stakeholders.

Lead design and implementation of scalable ETL/ELT pipelines, ensure data quality and governance, build dashboards and automated reporting, mentor engineers, troubleshoot production incidents, and improve platform reliability, performance, and observability to support analytics and business KPIs.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

Design and implement scalable AWS-based data pipelines and ETL using Python; integrate with APIs and microservices; build data models and data warehouses; optimize storage and processing for cost and performance; monitor data quality and availability; collaborate with data scientists and engineers; enforce data governance and privacy.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Transforms raw data into high-quality datasets through ingestion, transformation, modeling, and validation. Supports low- to moderately complex business problems while receiving guidance from senior data leaders. Combines data engineering, analytics, and business acumen to build accessible data workflows that support organizational decision-making.

Lead and manage data engineering and BI initiatives, develop and oversee data pipelines and reporting (Power BI), collaborate with stakeholders across functions, and mentor engineering staff. Role sits within KPMG India professional services environment.

Design, build, and maintain reports, dashboards, datasets, and data pipelines across on-prem and cloud (Snowflake/Lakehouse). Transform structured and unstructured data for analytics and AI/ML, ensure data governance and security, troubleshoot refresh/connectivity issues, optimize performance, translate business needs into technical solutions, and document processes.

Design, build and maintain data pipelines and ETL on cloud platforms (Databricks/Azure). Partner with product and architecture, translate requirements, perform root cause analysis, enforce code quality and CI/CD, conduct code reviews, mentor team members, and recommend emerging technologies.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Lead the design and delivery of a cloud-native healthcare data platform on AWS. Architect and implement scalable batch and near-real-time pipelines, lead Apache Iceberg lakehouse engineering, build reusable ingestion/transformation/validation frameworks, drive data quality, observability, and operational excellence, review architectures, conduct code reviews, mentor engineers, and collaborate across platform, analytics, and business teams.

The Data Engineer role involves data analysis, creating data models, and collaborating with business partners. Applicants need strong technical and analytical skills, particularly in SQL and data modeling.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Designs and refines data platform blueprints, collaborating with integration and data architects to align systems, models, and platform components. Leads performance and scalability optimization, troubleshoots complex platform issues, supports data quality and consistency, and coordinates cross-functional initiatives. Acts as a subject matter expert, makes team decisions, provides solutions across teams, and mentors junior engineers. Requires extensive Snowflake experience, cloud data architecture knowledge, SQL, data integration, and data modeling expertise.

Designs and refines scalable data platform blueprints, collaborating with integration and data architects to align systems, models, and data flows. Leads performance and scalability optimization, troubleshoots complex platform issues, supports cross-functional initiatives, makes team decisions, and mentors junior engineers. Requires strong Snowflake data warehousing, cloud data architecture, data integration, SQL, performance tuning, data modeling, and data quality expertise.

Develop and maintain data pipelines, integrate and process data from multiple sources, enforce data governance, troubleshoot data issues, and support Tableau dashboard creation. Collaborate with business stakeholders and analysts on end-to-end analysis, provide expertise on customer success data, extract data for business reviews, and mentor junior data engineers. Use Python, Airflow, SQL, Oracle, and Snowflake in a business intelligence environment.

Develop and support AWS cloud-native data lakes and ecosystems, scalable Databricks data pipelines, and enterprise data solutions. Configure and optimize Databricks clusters and jobs, implement production-grade pipelines using PySpark and SQL, and work with services including Glue, Athena, Redshift, and PostgreSQL. Apply data security and privacy practices, provide production support, improve team processes, and collaborate in a global onshore-offshore delivery environment.

Lead design and build scalable GCP data pipelines and ETL/ELT processes using BigQuery, DBT, Python, and Airflow. Ensure data quality, optimize performance, mentor junior engineers, collaborate with cross-functional teams, and implement best practices for analytics-ready datasets and CI/CD workflows.