Top Data Engineer Jobs in Hyderabad

Lead hands-on data platform engineering: design and optimize AWS-native data lake architectures using a data mesh model, build data pipelines, implement IaC (Terraform), CI/CD, automated testing, monitoring and data quality frameworks, mentor engineers, and collaborate with stakeholders to deliver scalable, reliable data solutions.

Design, build, and maintain scalable ETL/ELT data pipelines and platforms on Azure Databricks. Ingest and transform structured and unstructured data using PySpark and Spark SQL, manage ADLS Gen2 storage, ensure data quality and performance, and collaborate with data science and BI teams to support analytics and reporting.

Design, build, and operate large-scale production data pipelines and warehouses. Collaborate with stakeholders, write maintainable code, ensure data quality, provide production support, mentor junior engineers, and enforce data protection and documentation best practices.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

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.

Build, operate, and improve a scalable cloud data platform and AI/LLM enablement tooling. Provision infrastructure with Terraform, manage Kubernetes-hosted services (Airflow, APIs, AI services), develop CI/CD pipelines, support dbt and cloud warehouses, troubleshoot platform issues, optimize performance and cost, and create developer enablement and documentation.

Design, build, and operate automated data quality checks, profile deduplication, and reconciliation pipelines across loyalty platforms (mParticle, Braze, Snowflake, Azure SQL). Monitor data quality KPIs, validate integrations via REST APIs, remediate issues, and document audit trails and runbooks.

The role involves developing data governance policies, monitoring data quality metrics, collaborating with stakeholders, and managing risks within projects.

Design, build, and maintain scalable GCP data pipelines, data lakes/warehouses, and real-time/batch processing. Improve data quality, implement CI/CD/DevOps for pipelines, collaborate with data scientists and stakeholders, and support implementation and migration efforts.

The Software Engineer - Data Analyst / Developer will support the BI team by analyzing business needs, developing reports, dashboards in Tableau, ensuring data quality, and providing user support.

The Sr Data Engineer is responsible for acquiring, curating, and publishing data for analytical uses, ensuring data readiness and consistency, consulting on complex data projects, and developing scalable data pipelines and integrations.

The Senior Data Engineer builds and maintains enterprise data pipelines, implements data observability, and integrates with governance tools, ensuring data reliability and transparency.

Design and maintain ETL/ELT pipelines and SQL-based dimensional models (star schemas); build and support Power BI datasets and reports; define KPIs and data governance; troubleshoot production data issues; and develop backend services in Go to support contact center and digital analytics.

The Senior Data Engineer will design and optimize ETL/ELT pipelines, manage data integration, perform data validation, and support reporting solutions using various technologies.

Design, build, monitor and scale an enterprise data warehouse and pipelines. Lead data integration, ETL design, source-to-target mapping, reusable frameworks, automated testing/CI, and partner with stakeholders to deliver secure, governed analytics for product, marketing and HR use cases.

The role involves data modeling, troubleshooting complex issues, optimizing storage architectures and mentoring junior engineers while working with GCP and Python.

The Data Engineer will design, build, and maintain ETL pipelines and data products, utilizing big data technologies and ensuring efficient data architecture. Responsibilities include troubleshooting complex issues, developing scalable solutions, and managing databases and data security compliance.

As a Lead Data Engineer, you will design and manage data pipelines, develop ETL processes, and ensure data quality. Mentorship of junior engineers is also part of the role.

Lead Azure Data Engineer to design, build, and operate petabyte-scale data systems on Azure. Lead a data engineering team to develop ETL pipelines (ADF, Synapse, Databricks), perform data modelling, migrations, monitoring, security, and collaborate with BI/ML/Product teams while mentoring junior engineers and improving analytics and dashboarding.

The Senior Data Scientist & Engineer will build and manage data infrastructure, analyze product usage, build models, track metrics, run experiments, and communicate insights to guide product decisions.

Lead design, development, and optimization of ETL/ELT data pipelines on Databricks using PySpark and Spark SQL. Implement Delta Lake, Unity Catalog, incremental ingestion (Auto Loader/streaming), Databricks Workflows, and performance tuning. Build reusable accelerators, participate in client technical discussions, mentor junior engineers, and resolve production issues while promoting engineering best practices.

Design, build, and maintain ETL/ELT pipelines and data integration workflows to Snowflake using Azure Data Factory and DBT. Write optimized SQL, monitor and troubleshoot pipeline performance, enforce data quality and governance, and collaborate with stakeholders, analysts, architects, and DevOps in cloud-native environments.

Lead end-to-end customer data integration and migration projects using SQL, ETL, and scripting. Act as technical advisor, design tailored solutions, manage 10–15 projects, document requirements, mentor juniors, and collaborate with government stakeholders and cross-functional teams to improve and automate data workflows.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

The role involves managing data pipelines and platforms, ensuring data reliability, collaborating with AI teams, and leading a small team to deliver high-quality data solutions.