Top Data Engineer Jobs in Hyderabad

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 and implement scalable data engineering solutions and EDW data models. Build ETL/ELT pipelines (IICS) into Snowflake, optimize performance, enforce governance, support production deployments, conduct code reviews, troubleshoot issues, and collaborate with cross-functional teams to deliver robust, maintainable data platforms.

Design, build, and maintain scalable EDW data pipelines and models using Snowflake and IICS. Collaborate with cross-functional teams, enforce governance and standards, conduct code reviews, troubleshoot production issues, and drive continuous improvement of data engineering practices.

The Data Engineer will build and maintain ELT pipelines, develop dbt models, troubleshoot issues, and document processes while collaborating with a global team.

Design and maintain data pipelines using the Azure ecosystem. Optimize streaming and batch processing with Databricks and manage Medallion Architecture. Collaborate with stakeholders to ensure data quality and support analytics use cases. Implement testing automation and best practices for governance in cloud-based systems.

Support and operate enterprise data platform and pipelines: develop, monitor, and troubleshoot StreamSets/Snowflake/Python ingestion and transformation workflows, perform SQL analysis and pipeline testing, support Salesforce integrations, production incidents, CI/CD, and collaborate across engineering and data teams.

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.

Design, build, and optimize ETL/ELT data pipelines using Microsoft Fabric and Azure Data Factory. Implement and maintain data warehousing solutions, write and tune complex SQL queries, manage Azure resources, ensure data governance and pipeline reliability, and collaborate with stakeholders for analytics needs.

Senior Data Engineer responsible for designing, supporting, and optimizing ETL/ELT pipelines and data ingestion using SSIS, Apache NiFi and cloud data platforms (GCP/BigQuery). Provide L3 production support, incident management, performance tuning, automation, and API integrations while collaborating with data scientists and business teams to ensure data quality, availability, and operational efficiency in a 24x7 environment.

Ensure end-to-end data accuracy across Azure and Databricks platforms by designing and executing automated data quality, validation, and regression tests. Build Python/SQL-based validation frameworks, perform source-to-target reconciliation, metadata and lineage checks, embed checks into CI/CD pipelines (GitHub/Jenkins), create and manage test data, and collaborate with data engineers and analysts in Agile/Scrum to identify and remediate data quality defects.

Design, develop, test, and deploy scalable data and analytics applications. Lead data strategy, governance, risk management, and platform engineering for batch and streaming. Implement CI/CD and TDD, use cloud (AWS) deployments, leverage enterprise AI-assisted development tools, and ensure responsible AI and secure data handling.

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.

Lead development and support of Honeywell's Informatica Product 360 PIM/MDM: oversee inbound/outbound jobs, analyze and implement change requests, perform code migrations and upgrades, build PIM modules and data quality customizations, configure Power BI reports, guide downstream integration, run demos with stakeholders, and contribute to AI automation.

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, develop, and maintain data solutions and ETL/data pipelines. Ensure data quality, perform data modeling, integrate data across systems, and collaborate with stakeholders to translate requirements into scalable Snowflake-based deployments.

Design, develop, and maintain data solutions and ETL pipelines using PySpark. Ensure data quality, mentor team members, collaborate across teams, guide technical decisions, and monitor project progress to meet milestones.

Design, develop, and maintain data solutions and pipelines using Snowflake. Implement and optimize ETL processes, ensure data quality, monitor and troubleshoot pipelines, document workflows, collaborate with cross-functional teams, and mentor junior members to support scalable cloud data operations.

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Design, build, and maintain scalable ETL/ELT data pipelines and databases for analytics. Troubleshoot data integrity, translate business requirements into code, lead solution development and testing, train users, and support production deployments and vendor issue resolution.

Design, build, and maintain scalable ETL/ELT data pipelines and database systems using AWS and big-data tools. Ensure data quality and integration, translate business needs into solutions, lead development phases, test and document code, and train stakeholders.

Design, validate, and deliver high-integrity customer and partner foundation data using Oracle/SQL/PLSQL and Excel/Access. Perform EDA, identify anomalies and opportunities, validate third-party sources, apply AI techniques (predictive analytics, LLM, NLP) for data enrichment, and collaborate with cross-functional stakeholders, vendors, and IT in an Agile environment.

Designs, standardizes, migrates, transforms, validates and optimizes data across multi-database platforms. Builds and maintains ETL pipelines, supports client data lifecycles, troubleshoots production defects, contributes across SDLC, and maintains code in GitHub. Supports off-hours processing and ensures data quality and policy adherence.

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 pipelines and platforms. Lead POCs for emerging tech and AI-driven analytics, ensure data quality and governance, collaborate cross-functionally, and enable data consumption models for reporting and portals.