Top Data Engineer Jobs in Hyderabad

Design and implement ETL/data migration solutions for SAP S4 HANA ERP deployments. Enforce methodologies and MOS, create/update mappings, develop Informatica and SQL solutions, ensure data quality and reporting, manage multiple projects to SLAs, mentor teams, and introduce new data tools.

Design and build scalable GCP-based data pipelines and ETL/ELT processes using BigQuery, Python, DBT, and Airflow. Ensure data quality, performance, CI/CD, and documentation; mentor junior engineers and collaborate with cross-functional teams to deliver analytics-ready datasets.

Design, build, and optimize scalable data lakehouse solutions on Azure using Python, PySpark, ADF, Databricks, and Function Apps. Develop reliable data pipelines and products, collaborate with product and marketing teams, follow coding standards and CI/CD practices, and continuously improve data workflows and performance.

Design, develop, and maintain scalable PySpark/Python data pipelines; write complex SQL for transformations; build and manage ETL and data warehouse solutions; work with Hadoop/big data technologies; ensure data quality, reliability, and performance; collaborate with stakeholders and troubleshoot/optimize data workflows.

Design, build, and maintain scalable data pipelines and ETL/ELT processes using PySpark and Python. Optimize complex SQL queries, manage data warehouse solutions, and handle large-scale data processing with Hadoop/Big Data technologies. Collaborate with business teams to translate data requirements, ensure data quality, and tune performance of data workflows.

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.

Design, build, and maintain scalable GCP-based data ingestion and curation pipelines using Python, SQL, DBT/Dataform and GCP services (BigQuery, Dataflow, Pub/Sub). Implement data governance, security, IaC (Terraform), orchestration (Astronomer, Tekton), performance and cost optimizations, and collaborate with cross-functional teams to translate business requirements into reliable data assets and automation.

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.

Lead design and implementation of Databricks Lakehouse data platforms for healthcare. Build scalable ETL/ELT pipelines with PySpark/SQL, enforce data governance, security (HIPAA/PHI/PII), integrate clinical and claims sources, optimize performance and costs, and mentor engineering teams.

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.

Serve as a technical product conduit between product and engineering to translate business outcomes into build‑ready requirements, contribute hands‑on to SQL-based data analysis, schema definitions, metadata and pipeline validation, embed data quality and reconciliation checks, and support cloud data platform initiatives and delivery excellence across analytics, automation, and governance.

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, build, and maintain cloud-native ETL/ELT pipelines to Snowflake using Azure Data Factory and DBT. Develop optimized SQL for extraction and transformation, ensure data quality and governance, monitor and troubleshoot pipelines, and collaborate with analysts, architects, and DevOps to translate business requirements into technical solutions.

Design, develop, and maintain cloud data infrastructure, data pipelines, data models, and storage solutions on Azure. Ensure data quality, integrate with cross-functional teams, optimize distributed systems, and establish data engineering best practices.

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.

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, 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-based ETL/data pipelines in Snowflake. Extract and transform data from heterogeneous sources, optimize SQL performance, implement monitoring, and collaborate across teams. AWS and Python experience are advantageous.

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.

Design, develop, and optimize PL/SQL artifacts and Python ETL scripts; tune SQL and Oracle performance; ensure data integrity and availability; integrate systems via APIs; support production deployments and monitoring; collaborate with engineers and analysts; administer Oracle and PostgreSQL; use GitLab and Jira for version control and tracking.

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 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.

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.

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.

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.