Top Data Engineer Jobs in Hyderabad

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Lead technical direction for data platform capabilities: design and maintain data pipelines, reporting models, and data governance to support trusted metrics and AI/LLM agentic workflows. Drive architectural improvements, mentor engineers, collaborate on roadmaps, and represent technical capabilities externally.

Design, build, and maintain scalable batch and streaming data pipelines on GCP. Develop ETL/ELT workflows with BigQuery, Dataflow, and Cloud Storage, orchestrate with Cloud Composer, optimize SQL and BigQuery performance, monitor production pipelines, and collaborate with teams to deliver reliable data solutions.

Design, build, and maintain scalable ETL/ELT data pipelines using Azure Data Factory, Azure Databricks and PySpark. Implement data integration and MDM solutions, optimize SQL queries, support CI/CD via Azure DevOps/GitHub, and ensure data governance in regulated life-sciences environments while collaborating across teams and troubleshooting complex data issues.

Design, develop, and coordinate cloud data solutions using Snowflake, Python, and AWS. Support product releases, deployments, testing, and post-install support. Collaborate with architects and tech leads, troubleshoot issues, and maintain documentation. Contribute to agile squads, adopt new technologies, and enable Fidelity Wealth business data needs.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data solutions.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Perform data migration/refresh from production, support Python-based data refresh framework, run post-migration fixes, validate and reconcile data, monitor and resolve batch jobs and pipeline issues, perform performance checks, and coordinate with dependent teams.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data, monitor and validate data flows, collaborate with cross-functional teams, and document data workflows and governance.

Join the Event Streaming and Data Replication platform as a Data Engineer to design, build, and operate cloud-native data platforms. Work with Airflow, Spark, Kubernetes, and Python; deploy containerized apps on AWS/Azure; use IaC (Terraform/CFT/ARM); implement CI/CD (GitHub, Artifactory, Jenkins); configure monitoring and alerts; and collaborate across teams to deliver highly available, scalable solutions.

Design, build, and maintain scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.

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.

Develop and maintain data pipelines and applications using Python and Pandas, apply OOP and data structures, write and optimize SQL, and communicate with stakeholders. Familiarity with Agile, DevOps/CI-CD tools (Jenkins, Git, Jira), AWS, web frameworks (Django/Flask), and Snowflake is a plus.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors to ingest diverse data, implement DataOps and data CI/CD pipelines, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, document workflows, and work with large-scale distributed datasets and visualization tools.

Design, develop, deploy, and administer SSIS-based ETL solutions and data pipelines. Optimize SSIS packages and SQL performance, support data warehouse modeling, monitor ETL operations, provide L2/L3 production support, enforce data quality and governance, and collaborate with BI teams for reporting and migrations across environments.

Lead and mentor a data engineering team to design, build, and operate scalable GCP-based ETL/ELT pipelines, BigQuery data lakes/warehouses, and batch/real-time processing. Drive architecture, perform code reviews, implement CI/CD and IaC, collaborate with stakeholders, and manage delivery timelines and technical risks.

Design, build, and optimize ETL pipelines and data warehouses using SQL, AWS Glue, and S3. Investigate and resolve data issues, develop Python-based solutions and small web apps, and own requirements, design, and end-to-end delivery for change requests.

Build and operate scalable data pipelines, workflows, semantic models, APIs, and data products on an Azure-based platform. Establish data quality rules, monitoring, observability, testing, CI/CD, and release-management practices. Diagnose performance and data issues, conduct root-cause analysis, coordinate remediation, and maintain operational runbooks. Collaborate with engineering, governance, data owners, stewards, and business stakeholders to ensure reliable data flows, governance, security, and continuous improvement.

Build and operate a scalable data platform: design and maintain ETL/ELT pipelines, Bronze/Silver/Gold data layers, data models and semantic layers, manage migrations and schema changes, ensure data quality/observability, enable analytics and future event-driven/recommendation systems, and use AI-assisted workflows to accelerate engineering.

Designs, develops, and maintains Power BI business intelligence solutions. Responsibilities include data modeling, ETL, dashboard and report creation, data analysis, performance optimization, documentation, stakeholder collaboration, and end-user training. The role requires ensuring data accuracy, reliability, scalability, and actionable insights for data-driven decision-making.

Designs and maintains scalable data pipelines, ETL/ELT processes, data models, and storage solutions supporting analytics, machine learning, and business intelligence. Collaborates with cross-functional teams to deliver trusted datasets, owns data platform components, implements monitoring and observability, and advances data engineering standards, testing, documentation, CI/CD, and lakehouse architecture.

Build and optimize scalable cloud data solutions, including ingestion, transformation, curation, and ETL/ELT pipelines. Apply data modeling and warehousing best practices, automate workflows, monitor pipeline health, troubleshoot quality and performance issues, and optimize reliability and cost. Collaborate with Cloud Solution Architects, lead code reviews, follow controlled development procedures, and support CI/CD across Agile and Waterfall environments.

Designs and maintains data pipelines using ETL/ELT platforms, develops dbt models, and manages Snowflake workloads. Establishes data quality testing and monitoring, optimizes data performance and costs, supports CI/CD and infrastructure-as-code practices, participates in architecture reviews, and mentors junior engineers. Collaborates with analysts and data scientists to build reliable data models and transformation logic.

Design, develop, and deploy scalable cloud-based data pipelines and ETL/ELT processes. Build and optimize Snowflake and dbt solutions, apply data warehousing concepts such as dimensional modeling, CDC, and SCDs, and use workflow orchestration tools. Ensure data governance, security, and privacy best practices while collaborating with technical and non-technical stakeholders in an Agile environment.

Develop and optimize SQL-based data pipelines, build staging, ODS, and warehouse layers, create file ingestion and outbound workflows, validate and reconcile data, implement incremental processing and change data capture, tune performance, and support hybrid legacy-to-cloud data warehouse migrations.