Top Data Engineer Jobs in Hyderabad

The Sr GCP Data Engineer will work with cloud technologies to engineer data solutions, requiring over 5 years of experience in the field.

Design, build, and operate production ETL/ELT pipelines using Python and Pandas; author performant SQL for PostgreSQL and Trino; architect AWS data solutions (S3, Glue, Athena); implement Terraform-based IaC and automated CI/CD; embed security best practices; collaborate with product and analytics teams to deliver dimensional models, curated datasets, and Superset dashboards while working within Agile delivery.

Serve as a technical product-facing data engineer translating product requirements into build-ready engineering artifacts. Partner with product, architecture, and platform teams to design and validate data models, SQL queries, pipelines, APIs, and data quality checks. Contribute hands-on to schema definitions, metadata, pipeline validation, automation, and observability to enable reliable cloud data platform delivery and accelerate engineering execution.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

As a Data Engineer, you will design and maintain systems for processing large datasets, develop data pipelines, ensure data integrity, and optimize workflows for analytics and machine learning.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

The Data Engineer role involves designing and maintaining ETL pipelines, optimizing data storage solutions, collaborating with teams, and ensuring data accuracy and integrity.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

Design, build, and operate scalable ETL/ELT pipelines using open-source tools. Optimize databases and SQL, prototype new tools, support migration from Azure Databricks to AWS, ensure data quality, observability, CI/CD, and collaborate across distributed teams in Advertising and Market Research.

Lead modernization of a large-scale AWS data platform: design, build, and optimize scalable ETL/ELT pipelines, migrate Airflow/EMR to AWS-native services (Glue, MWAA), implement data lake/warehouse solutions, enforce data governance/lineage/metadata, build data quality and monitoring frameworks, automate deployments with IaC, and support BI (QuickSight) and Power BI migration, UAT, and production support.

Design, build, and operationalize AI-enabled data pipelines and production-grade AI applications using Python and Snowflake. Prepare and transform large datasets, develop features and embeddings, support deployment and monitoring of ML/LLM models, optimize Snowflake for AI workloads, and collaborate with data science and engineering teams to deliver scalable APIs and RAG-enabled data workflows.

Design, build, and maintain scalable data pipelines and ETL solutions. Collaborate with teams to gather requirements, ensure data quality and integrity, optimize data workflows, and support analytics and decision-making across the organization.

The Data Engineer is responsible for optimizing data architecture and pipelines, collaborating with teams to deliver data solutions, and building analytics tools for business insights.

Build and maintain large-scale data architectures and pipelines using AWS and big-data technologies. Develop ETL/streaming solutions, design data lakes, support analytics and ML use cases, implement data governance, optimize queries, and collaborate with product and data stakeholders to deliver scalable, reliable data platforms.

Design and build scalable batch and real-time data pipelines using Spark, Kafka, Java and SQL. Develop ETL/ELT frameworks, streaming solutions, data models and cloud data warehouses. Automate workflows with Airflow/DBT, monitor reliability, collaborate with Data Science and Engineering, and follow governance, security, and cost-optimization best practices.

Design, build, and maintain scalable data pipelines and models using Big Data and cloud technologies. Ensure data quality, security, SLA adherence, and collaborate with analytics and ML teams. Monitor, optimize, and document ETL/ELT processes.

Design, build, and maintain scalable data pipelines and architectures using cloud and streaming technologies. Develop ETL/streaming workflows, optimize performance and reliability, integrate multiple data sources, ensure data governance and security, and collaborate with analytics and product teams to support data needs.

Senior Azure Data Engineer responsible for designing and implementing end-to-end data pipelines using Azure Databricks, Azure Data Factory, and PySpark. Work with Delta Lake, Spark SQL, and data warehousing concepts to build scalable ETL/ELT solutions; collaborate on analytics and Power BI integration. Availability for 2pm-11pm/12am IST shift required.

Design, build, and maintain scalable ETL/ELT data pipelines and data models. Orchestrate workflows with Apache Airflow, implement transformations with dbt, use BigQuery and GCS, ensure data quality and performance, and collaborate with analytics teams to support BI and analytics initiatives.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Implement and maintain ETL/data‑warehousing solutions using IBM DataStage. Develop complex SQL and database solutions (Oracle/Teradata). Collaborate with team, provide technical leadership, communicate with stakeholders, and support Unix-based deployments.

Design and implement data models from business requirements. Develop PL/SQL packages, procedures, and complex SQL queries. Work onsite at Dell Hyderabad; participate in face-to-face interviews and meet short notice-period constraints.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

Design, develop, and integrate SAP MDG solutions using ABAP, REST APIs, IDocs, and ETL. Collaborate with cross-functional teams in Agile/SCRUM, maintain code in Git, support testing and automation, and provide on-call support for master data systems.

Lead asset-management analytics strategy and solutions: translate business needs into scalable data products, design modern data architectures (lakehouse, pipelines, integration), champion AI-driven transformation and generative/agentic AI use cases, ensure data governance/quality, and act as primary business-technology liaison to deliver performance analytics and attribution capabilities.