Top Data Engineer Jobs in Hyderabad

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.

Architects and operates large-scale distributed data platforms using Databricks and Spark. Responsibilities include designing Databricks deployments, optimizing Spark and Delta Lake workloads, implementing Infrastructure as Code, leading incident response, and establishing performance standards. The role requires production ownership and experience with data or cloud engineering, Databricks, Spark, and Infrastructure as Code. Streaming architecture, AI/ML workload enablement, and FinOps cost optimization are advantageous.

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.

Build and maintain ELT/ETL pipelines using Airbyte and Dagster, develop tested and documented DBT models, manage Snowflake warehouses and schemas, optimize SQL and data models, implement data quality and observability practices, and collaborate across analytics, product, and engineering teams. Automate deployments through Git-based version control and CI/CD workflows.

Architect and deliver enterprise Power BI analytics solutions, including semantic models, star schemas, DAX frameworks, Microsoft Fabric workloads, governance, security policies, and CI/CD deployments. Manage capacity performance, identify model inefficiencies, automate deployments, collaborate with onshore architecture teams, mentor junior staff, and ensure high-quality, timely delivery.

Build scalable data pipelines, maintain reliable data flows across the analytics ecosystem, and deliver high-quality data transformations. Requires advanced SQL expertise, including window functions, aggregations, recursive CTEs, dynamic SQL, pivoting, error handling, and query performance optimization using indexing, partitioning, and statistics.

Design, build, maintain, and troubleshoot scalable data pipelines, ETL/ELT workflows, data warehouses, lakehouse structures, and Azure-based data solutions. Develop optimized T-SQL logic, support ADF, SSIS, and Microsoft Fabric pipelines, validate data quality, resolve integration issues, document data flows, and assist with deployments, testing, production operations, and enterprise data modernization.

Design and maintain Snowflake Silver and Gold data models, develop optimized SQL transformations, and build dashboards using Sigma Computing or other BI platforms. Translate stakeholder requirements into scalable reporting solutions, validate data quality, troubleshoot discrepancies, document business logic, and improve analytics automation and performance. The role also supports data warehousing, dimensional modeling, ETL/ELT processes, and cloud-based analytics architectures.

Build and operate cloud-native Azure data pipelines using Fabric Dataflow Gen2 and Event Hubs. Develop Bronze-Silver-Gold Delta table layers, maintain SQL evaluation logic in Synapse Analytics, validate data quality and schemas, verify lineage, and support parallel-run comparisons between automated outputs and source reports.

Leads the architecture, development, deployment, and optimization of scalable cloud-native data pipelines and warehouses. Establishes engineering standards, governance, security, privacy, and compliance practices; mentors engineers; conducts technical reviews; makes architectural decisions; and translates business needs into maintainable data solutions.

Design and implement scalable logical and physical data models, data warehouses, and ETL/ELT ingestion frameworks using Snowflake Medallion architecture. Develop optimized SQL and RDBMS schemas, establish data quality, lineage, governance, and DataOps practices, and collaborate with business and engineering teams. Document data structures and transformation logic while supporting distributed-team collaboration and stakeholder management.

Designs, builds, and maintains scalable data pipelines, platforms, and integrations across cloud environments. Develops batch and real-time ETL/ELT workflows, transforms structured and unstructured data, and supports analytics, business intelligence, and AI/ML use cases. Responsibilities include data quality, governance, security, monitoring, optimization, documentation, testing, deployment, and collaboration with architects, analysts, and stakeholders.

Design, develop, optimize, and support SQL Server databases, T-SQL objects, ETL workflows, and enterprise data pipelines. Reverse engineer legacy code and data flows, troubleshoot production issues, improve performance, ensure data quality, support integrations and deployments, and document technical processes. Collaborate with business analysts, architects, and application teams on data solutions, modernization, testing, and production support.

Build and optimize scalable cloud-based data ingestion, transformation, and curation pipelines. Apply data modeling, warehousing, ETL/ELT, orchestration, monitoring, and performance optimization practices. Partner with Cloud Solution Architects to implement approved designs, lead code reviews, troubleshoot data quality issues, and automate workflows. The role requires strong SQL and Python skills, Snowflake and Databricks experience, CI/CD familiarity, and independent cross-functional collaboration.

Designs and maintains end-to-end data pipelines using ETL/ELT tools, develops dbt models, and manages Snowflake workloads. Establishes data quality testing and monitoring, optimizes performance and costs, supports CI/CD and infrastructure-as-code practices, participates in architecture reviews, and mentors junior engineers.

Leads the architecture, modernization, migration, and optimization of Snowflake-based data platforms. Designs secure and scalable data models, establishes governance and access controls, guides cloud integrations, and provides technical leadership across teams. Collaborates with stakeholders, monitors performance, recommends Snowflake enhancements, and mentors engineers, developers, and analysts. Requires extensive data engineering or architecture experience, Snowflake expertise, cloud platform knowledge, and experience delivering data warehouse modernization projects.

Designs and delivers enterprise Power BI dashboards and semantic models using Direct Lake and Azure Fabric Lakehouse data. Develops DAX measures, time-intelligence patterns, compliance and audit reporting, and domain-specific KPIs. Validates model and report accuracy, supports user acceptance testing, manages workspaces, publishes datasets, allocates access, and collaborates with Data Architects on governance and enterprise data model alignment.

Leads the design, development, and deployment of scalable cloud-native data pipelines for enterprise analytics and reporting. Defines data engineering standards, reusable frameworks, governance, security, and compliance practices. Mentors engineers, conducts code and design reviews, makes architectural decisions, translates business needs into technical solutions, and owns end-to-end data initiatives in an Agile environment.

The Snowflake Data Engineer is responsible for designing data models, creating data integration solutions, and maintaining data pipeline architecture while leveraging Snowflake and Python. The role requires strong interpersonal skills and the ability to thrive in a client-facing environment with a focus on exceeding expectations and solving problems creatively.

Lead and contribute hands-on to the IQ-EQ Data Platform: design, build, optimize, and maintain large-scale data pipelines and production-ready data models. Collaborate with engineers, architects, analysts, and data scientists to ensure scalable, reliable data delivery, mentor teams, and champion best practices across a global, hybrid environment.

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, optimize, and maintain scalable GCP data pipelines and ETL/ELT solutions using Python and SQL. Build data processing and transformation workflows, perform data modeling and validation, troubleshoot production issues, and implement data quality, security, and governance practices. Collaborate with business and technical teams, participate in code reviews, and contribute to technical architecture and design decisions.

Develops and optimizes SQL Server queries, stored procedures, and data processes. Performs data validation, reconciliation, migrations, production fixes, monitoring, incident troubleshooting, root cause analysis, and data quality checks. Builds SQL Server Agent, PowerShell, and Python automation to improve operational efficiency and data reliability. Coordinates with development teams to resolve defects and maintains incident documentation.

Design, build, and maintain dbt models and Snowflake data warehouses, own CI/CD and orchestration for pipelines, implement data quality tests, partner with stakeholders to translate requirements into production-ready data models, and optimize performance and observability.

Design, develop, and maintain interactive Power BI/Fabric dashboards, conduct data analysis to surface trends and insights, collaborate with stakeholders to gather requirements, ensure data quality through ETL and modeling, and present findings to inform business decisions.