Top Data Engineer Jobs in Hyderabad

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.

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

Build and optimize scalable data pipelines on Azure Databricks using Python, SQL, and Spark. Collaborate with analysts, developers, and QA to design and execute test plans, automate testing, and integrate LLM/GenAI tools for test planning and scripting while following SDLC practices.

Develop data pipelines for analytics and reporting, optimize workflows, assist in data ingestion, monitor job performance, and collaborate on solutions.

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.

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.

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.

Design, develop, and maintain data solutions and ETL pipelines to generate, collect, process, and migrate data. Ensure data quality, implement data architecture, collaborate with stakeholders to gather requirements, and contribute to scalable data strategy and cross-functional problem solving.

Design and implement scalable, fault-tolerant big-data and cloud-based data applications to support low-latency fraud analytics. Partner with architects, data scientists and product teams to build pipelines, integrate AI services, ensure data management standards, and drive production deployments and automation in a scaled Agile environment.

Design, develop, and maintain end-to-end data solutions and pipelines. Implement ETL/ELT processes, ensure data quality, integrate and process data across cloud platforms, collaborate with stakeholders, mentor juniors, and support change, incident, and problem management to embed data-driven solutions.

Lead architecture, design, and delivery of enterprise Snowflake data platforms. Build and operate ingestion and transformation pipelines (batch and streaming), define data modeling and governance standards, implement CI/CD and IaC, optimize performance and cost, enforce security/compliance, and mentor engineers to deliver scalable data products for analytics, AI/ML, and operational use cases.

Design, build, and maintain data pipelines and ETL processes using Databricks. Ensure data quality, optimize workflows, collaborate with stakeholders, document processes, and mentor junior team members to deliver scalable cloud-based data solutions.

Design, develop, and maintain data solutions and ETL pipelines using Snowflake. Ensure data quality, lead and mentor the team, collaborate across teams, optimize workflows, and resolve data issues to support organizational data needs.

Design, develop, and maintain data solutions and ETL pipelines, ensure data quality, optimize data workflows, collaborate with cross-functional teams, document processes, and mentor junior engineers to support scalable data infrastructure.

Design, develop, and maintain data pipelines and ETL processes using Databricks and Azure services. Ensure data quality, optimize workflows, document processes, troubleshoot integrations, and collaborate across teams while mentoring junior members.

Design and build high-performance, real-time data pipelines and infrastructure for agentic AI. Implement, test, document, and deploy components using Python, SQL, AWS, Terraform and CI/CD. Provide estimates, own delivery, debug production issues, and optimize performance and cost while collaborating across teams.

Design, develop, test and support end-to-end Azure data analytics solutions. Build scalable, optimized data pipelines (Fabric, ADF, Databricks, Delta), perform SQL/PySpark transformations, implement CI/CD, publish Power BI dashboards, and mentor peers.

Design, implement, test, and deploy real-time data pipelines and infrastructure on AWS. Build CI/CD pipelines, write clean Python code, support ingestion and data modeling, and ensure secure, documented, production-ready components while collaborating with stakeholders.

The Data Engineer will design and build data pipelines, integrate data from various sources, optimize workflows, and guide data strategies, supporting project management and best practices.

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.

Develop, enhance, and support ETL/data pipelines using Sparkola, PySpark and SQL. Build and manage batch and ad-hoc pipelines via Sparkola Job Server, use Cloudera Machine Learning for development, validate datasets for downstream analytics (QlikSense/Superset), engage stakeholders to translate requirements, support production issues, and follow JIRA-driven delivery and governance controls.

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.

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.

Design, implement, and optimize scalable, secure cloud-based Big Data solutions and pipelines (batch and near‑real time). Troubleshoot Data Lake/Data Mesh environments, tune NoSQL/distributed jobs, produce documentation, and provide technical guidance, training, and client-facing demos.