Top Data Engineer Jobs in Hyderabad

Develops, tests, and maintains secure, scalable data pipelines and architectures. Updates logical and physical data models, supports enterprise data controls, applies advanced SQL and NoSQL knowledge, performs statistical data analysis, and customizes tools to generate business or customer-requested outputs. Uses authorized AI capabilities to accelerate pipeline design, testing, documentation, and control validation while ensuring output accuracy, data sensitivity compliance, traceability, and auditability.

Designs, develops, and scales secure data pipelines and platforms across enterprise data warehouses, lakes, and lakehouses. Leads cloud modernization and migration from on-premises systems using Spark, Hadoop, AWS, Airflow, and database technologies. Responsibilities include architecture, coding, testing, deployment, production support, data governance, observability, incident resolution, technical leadership, mentoring, and evaluating emerging data technologies.

Lead the architecture and engineering standards for scalable data platforms and pipelines. Provide technical direction, conduct design reviews, and oversee Databricks administration, CI/CD, DataOps, observability, deployment, migration, and upgrades. Resolve complex platform challenges, mentor engineers, strengthen code quality, and partner with stakeholders on roadmaps and technical trade-offs. The role requires deep expertise in Databricks, Spark, Python, PySpark, dbt, and engineering governance.

Design, develop, and maintain data pipelines and ETL processes using Databricks, Azure, and PySpark. Ensure data quality, support data migration and deployment, optimize workflow performance, troubleshoot integration issues, and document data processes. Collaborate with cross-functional teams, contribute to technical solutions, and support junior team members while serving as a subject matter expert.

Designs, develops, tests, deploys, and supports data pipelines, ETL/ELT processes, data stores, models, and BI solutions. Responsibilities include SQL development, data architecture, quality and governance, production support, troubleshooting, documentation, requirements gathering, testing, deployment, user training, and collaboration with business, IT, data science, architecture, and database teams.

Develop secure, scalable ETL and data pipelines using DMExpress and modern cloud and big-data technologies. Drive automation across data ingestion, movement, and access workflows; enforce software development and ETL engineering standards; troubleshoot production incidents; and improve processes. The role requires collaboration across distributed teams, independent execution, analytical debugging, root-cause analysis, technology evaluation, and proof-of-concept development.

Develop and maintain batch and streaming data pipelines on Google Cloud. Build complex data transformations, orchestrate ETL workflows, perform data migrations, and maintain data warehouses and data lakes. The role requires expertise in BigQuery, Composer, GCS, Cloud SQL, Dataflow, Dataproc, IAM, Python, SQL, and PySpark, along with strong database, data modeling, communication, and problem-solving skills.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, reliability, and secure migration across systems. Collaborate with cross-functional teams, troubleshoot integration issues, optimize pipeline performance, document workflows, and support junior team members while contributing as an independent subject matter expert.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, support data migration and deployment, troubleshoot data flows, and collaborate with multiple teams. Serve as a data engineering subject matter expert, make team decisions, mentor junior engineers, and improve data processes. The role requires Google Cloud Platform architecture expertise, with Google Looker experience preferred.

Design, develop, and maintain scalable data transformation, integration, and ELT pipelines using dbt and Fivetran. Own pipeline architecture, data modeling, testing, documentation, deployment, performance optimization, and data quality. Collaborate with architects, analysts, and stakeholders, resolve complex engineering issues, support cloud data platforms, and mentor junior engineers.

Design, develop, and maintain data solutions, pipelines, and ETL processes across cloud systems. Ensure data quality, optimize SQL and data processing performance, troubleshoot and orchestrate workflows, and implement comprehensive testing strategies. The role requires independent execution across data engineering and cloud DevOps projects, technical problem-solving, and collaboration with cross-functional teams to improve data accessibility, reliability, and organizational data strategy.

Designs and builds end-to-end data foundations, pipelines, ETL/ELT processes, and data integration solutions using Azure Databricks and related technologies. Ensures data quality, governance, accessibility, and efficient deployment across systems. Acts as a subject matter expert, mentors junior engineers, supports data strategy implementation, coordinates operational processes, and communicates findings to technical and business stakeholders.

Design, develop, and maintain data foundations, pipelines, ETL/ELT processes, and data integration solutions using Azure Databricks and related technologies. Ensure data quality, modeling, governance, and accessibility across systems. Collaborate with technical and business stakeholders, mentor junior engineers, support change and incident management, and drive data strategy implementation and efficiency improvements.

Lead the development and refinement of current and new data models. Collaborate with business representatives, data owners, application designers, architects, and technical teams to translate complex requirements into effective relational and dimensional models. Support database design, data warehousing, governance, and metadata management. Act as a subject matter expert, make team decisions, mentor junior staff, resolve cross-team issues, and drive continuous improvement in data architecture and modeling processes.

Builds and maintains scalable batch and streaming data pipelines, data models, data flows, API integrations, and data access methods across on-premises and cloud environments. Translates business requirements into technical solutions, performs QA automation and data validation, supports reporting and AI/ML use cases, applies governance standards, and delivers moderately complex data projects with limited supervision.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, flows, APIs, integrations, and access methods while translating business requirements into technical solutions. Cleanses and validates data for reporting, dashboards, analytics, and AI/ML use cases. Applies data governance, code management, and architectural standards while executing moderately complex projects with limited supervision.

Build and maintain scalable ETL/ELT pipelines, data ingestion workflows, backend services, APIs, and database systems. Clean, transform, validate, and model structured and unstructured data while implementing quality controls, monitoring, and production reliability. Develop Agentic AI and workflow automation integrations, deploy applications across cloud and on-premises environments, and collaborate with customers and cross-functional teams to deliver AI/ML solutions from proof of concept to production.

Architects and delivers scalable cloud data, analytics, AI/ML, and data science solutions. Leads enterprise data architecture, modeling, platform standards, integrations, and technical design decisions. Troubleshoots complex issues, reduces technical debt, collaborates with business and technical stakeholders, and mentors engineers, data scientists, and product owners. Evaluates emerging technologies and provides technical leadership across globally distributed teams.

Leads and mentors data and AI engineers while designing scalable cloud data platforms, batch and real-time pipelines, data models, governance, and analytics solutions. Owns architecture, development, deployment, reliability, observability, CI/CD, production support, and continuous improvement. Partners with stakeholders to translate business needs into secure, maintainable technical solutions and provides hands-on technical leadership across the full delivery lifecycle.

Build and support scalable batch and streaming data pipelines, cloud-native data platforms, data lakes, warehouses, and real-time processing systems. Own data initiatives from solution design through deployment, monitoring, performance tuning, and production support. Develop ETL/ELT pipelines, data models, and integration frameworks while collaborating with product, analytics, platform, and DevOps teams. Troubleshoot production issues, improve reliability, and contribute to documentation and engineering best practices.

Supports the design, development, and maintenance of data product pipelines under senior engineers’ guidance. The role involves using existing data management and knowledge systems, analyzing and interpreting data, identifying process and system improvements, supporting prescribed engineering and project management activities, and assisting with change management. It also includes coordinating short-term team schedules, developing personal capabilities, and advising managers on established procedures.

Build and maintain data pipelines using Databricks, Python, SQL, and Spark. Apply data modeling, cloud data services, version control, and CI/CD practices. Support streaming pipelines, workflow orchestration, data governance, security controls, and downstream BI requirements. Experience with AWS or Azure is required, while Databricks certification, streaming technologies, orchestration tools, and Power BI exposure are preferred.

Designs, develops, deploys, and supports scalable Azure data pipelines using Azure Data Factory, Databricks, Spark, PySpark, Python, SQL, and Delta Lake. Responsibilities include data ingestion and transformation, quality validation, performance optimization, monitoring, CI/CD, production troubleshooting, technical documentation, and collaboration with architects, analysts, BI developers, and engineering teams.

Designs and leads large-scale data engineering and ETL modernization projects, migrating data warehouses, data lakes, and analytics platforms to GCP. Responsibilities include gathering requirements, designing and deploying scalable pipelines, integrating diverse data sources, troubleshooting production issues, tuning performance, coordinating teams, and monitoring workflows. The role requires expertise in GCP data services, cloud ETL, infrastructure as code, DataStage, SQL, Python or Java, and mainframe-to-cloud migration.

Designs, architects, and optimizes scalable batch and real-time data pipelines, cloud data platforms, ETL frameworks, data models, and storage solutions. Owns data products end to end, including reliability, security, testing, monitoring, incident management, and continuous improvement. Partners with business and technical stakeholders to deliver sustainable data solutions, provides architecture and technical guidance, and mentors data and AI engineers. Requires hands-on AWS, big data, programming, database, and AI platform expertise.