Top Data Engineer Jobs in Hyderabad

Design, build, and operate scalable cloud-native ETL/ELT pipelines and data platforms (Databricks, Snowflake) ensuring data quality, governance, performance, and enablement of analytics and AI/ML workloads. Partner with stakeholders, apply data modeling and transformations, modernize legacy ETL, follow CI/CD and secure engineering practices, and mentor junior engineers.

As a Data Engineer, you will develop and maintain scalable data pipelines using AWS, support data processing, and collaborate with teams to deliver high-quality data solutions.

Lead and mentor multiple software and data engineering teams to deliver scalable, automated, and governed data platforms and APIs. Drive SDLC automation, enterprise AI-assisted development adoption, data governance, and cross-team technical strategy to improve reliability, performance, and reuse across the bank.

Design, build, and maintain cloud-based data management and ETL systems (large-scale/big data). Collaborate with data scientists to prepare, transform, and load data, ensure data quality, architect storage and access patterns, and deliver analytics-ready datasets and presentation layers across the enterprise.

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.

Design, estimate, and implement data ingestion pipelines into data lakes/warehouses using Spark/PySpark and Databricks; optimize cost and CI/CD automation; support streaming (Kafka), Delta tables, data quality, and cloud resource utilization (AWS/Azure). Participate in planning, stand-ups, and cross-team delivery.

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.

Hands-on technical leader who designs, implements, and operates cloud-native, event-driven ELT/ETL and real-time streaming pipelines (Ab Initio, Spark, Kafka) and Lakehouse infrastructure on AWS. Own end-to-end delivery, modernize legacy ETL to cloud-native architectures, enforce engineering standards and CI/CD, mentor engineers, and collaborate with architects and product owners in a SAFe environment for high-throughput, low-latency financial data products.

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.

The FBS Data Engineer is responsible for acquiring, curating, and publishing data for analytical and operational purposes, creating ready-to-use data for consumers, and managing data projects of intermediate complexity with minimal supervision.

Design, develop, and maintain Snowflake-based data solutions and ETL pipelines. Ensure data quality, optimize pipeline performance, document workflows, and collaborate with teams while mentoring juniors.

Design, develop, and maintain scalable data solutions and ETL pipelines. Ensure data quality, integrity, and security. Lead and mentor a team, coordinate cross-functional efforts, and make technical decisions to optimize data workflows and system performance.

Design, develop, and maintain data solutions and ETL pipelines to collect, process, migrate, and deploy data across systems. Ensure data quality, build scalable data models, collaborate with stakeholders to gather requirements, and act as a subject-matter expert contributing to the organization's data strategy.

Design, build, and maintain ETL/ELT data pipelines to load and transform large-scale data into Snowflake using Azure Data Factory and DBT. Write optimized SQL, troubleshoot and optimize pipeline performance, enforce data quality and governance, and collaborate with analysts, architects, and DevOps in a cloud-native environment to translate business requirements into technical solutions.

Design, develop, and maintain data solutions and ETL pipelines using PySpark. Ensure data quality, optimize workflows, troubleshoot data issues, document processes, collaborate with stakeholders, and mentor junior team members.

The Principal Data Engineer leads data engineering initiatives, designing and optimizing data platforms and pipelines, guiding adoption of technologies, mentoring teams, and aligning with business goals.

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.

Lead an offshore data engineering team to design, build, and operationalize scalable data pipelines and models. Implement ETL/ELT with dbt and IICS, use Google Cloud data services, ensure data quality and optimization, and act as customer–offshore technical liaison.

Design, build, and maintain scalable ETL/data pipelines and data models using Databricks, Python, SQL, and cloud platforms. Deploy infrastructure with Terraform, collaborate with stakeholders to deliver trusted datasets, monitor and optimize pipelines, and drive modern data engineering practices and platform enhancements.

Design, build, and maintain scalable ETL pipelines and data models using Python and SQL. Integrate multiple data sources, support cloud data platforms, ensure data quality and testing, and collaborate with data scientists, analysts, and stakeholders to deliver business-ready datasets.

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.

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.

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.

Support development, monitoring, and troubleshooting of ETL/ELT pipelines and data marts. Assist with data modeling, profiling, lineage, documentation, and incident resolution. Collaborate with business partners and senior engineers to implement data solutions, support data quality, and participate in cloud modernization and migration efforts.

Design and implement data migration and transformation solutions for SAP ERP/S4 HANA deployments using Informatica and SQL. Lead data migration strategy, enforce best practices and MOS, produce mapping and reporting, mentor teams, and support multiple SAP projects to meet SLAs and data quality standards.