Top Data Engineer Jobs in Hyderabad

Design, build, optimize, and support production-grade Snowflake data pipelines and dbt models with robust quality checks. Implement SQL/Python transformations, pipeline automation, AI integration for LLM workflows, and adopt AI-native tooling to ensure reliable enterprise analytics for executive reporting.

Design, build, and maintain scalable Snowflake/dbt data pipelines and architectures for enterprise reporting. Implement production-grade SQL and Python transformations, pipeline automation, and data quality tests. Instrument pipelines for metric reliability and integrate AI/LLM outputs to support analytics and executive reporting.

Design, build, and maintain Power BI semantic models, reports, and dashboards supporting NPS, TAC analytics, and customer health. Develop Power Automate workflows, author advanced DAX, collaborate on Snowflake/dbt optimization, enable configurable dashboard hierarchies, and help evaluate Microsoft Fabric and AI-augmented BI features for executive reporting and automated insight delivery.

The Data Engineer leads the development and deployment of MDM data models, ensuring data quality and compliance while mentoring team members and collaborating with other technical teams to improve enterprise data architecture.

Design, build, and maintain scalable batch and streaming ETL/ELT pipelines, production data services (REST/gRPC), and cloud-based data infrastructure. Implement CDC and time-travel data architectures, ensure data quality with tests and CI/CD, optimize data models for OLTP/OLAP, and collaborate cross-functionally to deliver reliable production data systems.

Design, build, test, and implement data products for finance (P&C insurance) use cases. Translate business data stories into technical requirements, perform data analysis and profiling, and deliver solutions using DBT, Snowflake, SQL, Python/PySpark, shell scripting, and Power BI within agile teams.

Responsible for designing and developing a GCP data ecosystem, creating data pipelines, optimizing data architectures, troubleshooting issues, and mentoring junior engineers.

Lead discovery, assess current Power BI and Fabric assets, design target Fabric architecture, and execute migrations (reports, datasets, lakehouses, warehouses, pipelines). Optimize models, refactor datasets (Import→Direct Lake), tune performance, and collaborate with Microsoft Support and implementation partners to establish governance and migration runbooks.

Lead the design and delivery of data-driven solutions for commercial strategy in life sciences, guiding cross-functional teams, developing data products, and providing mentorship.

The role involves designing and building data infrastructure, developing ETL/ELT applications, collaborating with teams, and improving engineering processes for analytics and AI capabilities.

Lead data engineering teams in building cloud-based data pipelines, optimize infrastructure performance, manage data quality, and support team development.

Designs, develops, integrates and tests data product features; manages development/operation of data management systems; coordinates across teams; analyzes diverse data sources to inform business decisions; recommends and implements improvements to processes, security, and knowledge management; delivers medium-scale projects and coaches colleagues.

Support design, development, and maintenance of data product pipelines under senior guidance. Build and operate data management systems using GCP BigQuery and Python, analyze data, identify process and IT security improvements, support project activities, coach colleagues, and assign short-term tasks to meet timelines in a hybrid Hyderabad technology centre.

The role involves designing scalable data pipelines and architectures using Microsoft Fabric, client engagement for requirements gathering, and optimizing data solutions for analytics and reporting.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

The BI Developer will design and develop business intelligence solutions, collaborating with stakeholders to ensure data accuracy, create visualizations, and optimize reporting performance across BI platforms.

Design, build, and maintain scalable ETL pipelines and data models for analytics and ML. Ensure data quality, optimize performance, enforce governance and security, and collaborate with stakeholders to deliver reliable, production-ready data for reporting and data science.

Deliver data engineering and analysis work using Python, PySpark, SQL and data platforms (Airflow, Trino, Hive). Produce LLD, coordinate offshore teams, follow Agile/Scrum, and work with container platforms (Linux, OpenShift, Kubernetes) and visualization (Superset). Banking domain experience is a plus.

Design, build, and maintain cloud data pipelines and streaming solutions using Java, Kafka, AWS services, SQL/warehouse technologies, and Python. Implement CI/CD, automated testing, and resilient microservice-based systems. Collaborate in a small agile team and ensure secure identity/authentication controls.

Design, implement and maintain high-volume online and offline batch and transactional data applications using Java and Spark. Build scalable data stores, write scripts, call APIs, and author complex SQL. Maintain code and database infrastructure, ensure data integrity, perform data cleaning, visualization and reporting, and support model training in an agile environment.

Design, build and maintain data solutions using Oracle SQL/PLSQL and Informatica ETL. Develop backend and APIs with Java/Spring Boot, work with XML/JSON, web frameworks (Angular/React), and operate in Unix environments. Use cloud/container tooling (AWS, Kubernetes, Docker, Rancher), Splunk for logging, follow Agile, collaborate across distributed teams, and support migrations and CI/CD pipelines.

Design, build and optimize ETL pipelines and cloud data workflows using Python, PySpark, AWS Glue and Lambda. Implement data models and schemas in PL/SQL/PostgreSQL, integrate AWS services (SNS, Step Functions), and troubleshoot pipelines for performance, scalability and reliability.

Design, build, and maintain ETL pipelines and frameworks on AWS. Deploy and troubleshoot containerized applications (Docker, EKS), support CI/CD and build tooling, work with SQL/Oracle and ORM tools, and use job schedulers like Autosys/Airflow. Collaborate in Agile/SCRUM environments.

Design, build and maintain ETL mappings and data models; write SQL and Snowflake queries; manage ETL/infrastructure on AWS (EC2); use Informatica, Control-M and ServiceNow; script in Python/Shell/PowerShell; collaborate with onshore/offshore teams and define BI delivery strategy.