Top Data Engineer Jobs in Hyderabad

Designs and maintains scalable ETL/ELT pipelines using Databricks, Snowflake, Python, Teradata, and Airflow. Builds data solutions for reporting, analytics, and AI/ML workloads; performs data quality validation, analysis, tuning, and operational monitoring. Collaborates with business and technical teams, supports deployments and issue resolution, applies CI/CD practices, and ensures data governance, security, and healthcare regulatory compliance.

Design, develop, and support large-scale cloud data solutions using Databricks, Snowflake, Azure, SQL, and Python. Build and optimize ETL/ELT pipelines, implement AI-powered solutions, and support production operations. Lead incident resolution, root cause analysis, code reviews, delivery planning, and continuous improvement. Mentor team members while ensuring data quality, security, performance, governance, and operational reliability.

Supports the design, development, maintenance, testing, and release of data product pipelines. Performs data analysis, validation, reconciliation, reporting, quality management, and defect investigation using established tools and procedures. Identifies process and system improvements, supports data management and project activities, collaborates with stakeholders, assigns short-term team schedules, and coaches others while developing technical capabilities under senior engineering guidance.

Owns the end-to-end design, development, and delivery of batch and real-time data pipelines using Databricks, Spark, Python, and PySpark. Leads dbt transformation architecture, CI/CD, DataOps, observability, streaming, platform optimization, deployments, and migrations. Mentors engineers, leads technical reviews, and partners with stakeholders on requirements and delivery trade-offs. Requires strong cloud, SQL, distributed processing, and data engineering expertise.

Design, develop, and optimize Azure-based data engineering solutions supporting analytics, reporting, and AI initiatives. Build scalable ETL/ELT pipelines with Azure Data Factory, Microsoft Fabric, Databricks, Spark, PySpark, and Python. Develop lakehouse architectures, data models, governance, quality checks, monitoring, and CI/CD automation. Collaborate with clients and technical stakeholders, troubleshoot production issues, modernize legacy pipelines, and ensure reliable, secure, cost-efficient data platforms.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, Python, SQL, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support Databricks platform design, cloud-native storage, orchestration, governance, security, and performance tuning. Collaborate with architects, data scientists, analysts, and engineers to ensure reliable, secure, and accessible data across systems.

Designs and maintains scalable ETL/ELT pipelines using SQL, Python, and cloud data platforms. Integrates data from multiple sources, implements quality and monitoring controls, troubleshoots performance issues, supports data modeling for analytics and machine learning, and follows security and governance practices. Collaborates with engineers, data scientists, and stakeholders while documenting pipelines and exploring AI-assisted development tools.

Own the end-to-end data platform, including BigQuery warehouse architecture, dbt models, ingestion pipelines, identity resolution, analytics marts, reverse-ETL activation, data quality, governance, orchestration, and CI/CD. Partner with engineering, analytics, product, marketing, and external data teams to deliver accurate, trusted, well-documented data. This is a hands-on Staff Data Engineer role requiring advanced SQL, Python, warehouse design, and operational data platform expertise.

Develop and lead Oracle PL/SQL and Informatica PowerCenter ETL solutions, including database components, data warehouse workflows, performance tuning, and high-volume data processing. Responsibilities include technical design, coding standards, code and workflow reviews, stakeholder collaboration, delivery estimation, production support, incident resolution, mentoring, and troubleshooting data and ETL issues.

Design, develop, and operate production-grade Databricks data pipelines integrating REST APIs and enterprise systems. Build dimensional models, advanced SQL transformations, Python utilities, data quality frameworks, and governed assets in Unity Catalog. Manage orchestration, monitoring, error handling, performance optimization, production troubleshooting, CI/CD, and data governance. Preferred experience includes Azure data services, Medallion Architecture, Delta Live Tables, telemetry ingestion, and DevOps practices.

Design, develop, optimize, and maintain scalable AWS data processing and ETL/ELT pipelines using Python, PySpark, Apache Spark, and SQL. Responsibilities include data extraction, transformation, validation, performance tuning, integration across databases, APIs, and cloud storage, monitoring, error handling, and data quality management. The role collaborates with engineering, architecture, analytics, and business teams while following Git, Agile, and software development best practices.

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.

Analyze and optimize SQL and Spark SQL queries, support ETL setup and data validation, create BI reports and dashboards, perform data quality checks and root cause analysis, troubleshoot Java logs, and collaborate with cross-functional teams. The hybrid role requires working virtually and in person at a TransUnion office at least two days per week.

Leads enterprise data engineering strategy and designs scalable cloud and hybrid data architectures. Responsibilities include building high-volume ETL/ELT pipelines, modernizing data platforms, implementing governance and security, supporting streaming analytics and machine learning workloads, and establishing DataOps practices. The role provides technical leadership and mentorship, partners with data science and product teams, translates business requirements into technical solutions, and develops REST APIs and Azure PaaS solutions.

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.

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.

Design and implement scalable data architectures and end-to-end batch and real-time ETL/ELT pipelines across GCP and Snowflake. Develop dimensional models, optimize queries and costs, execute data migrations, integrate diverse data sources, and implement security, governance, monitoring, lineage, and quality controls. Partner with data scientists, analysts, product, and business teams to translate requirements into reliable technical solutions.

Build, modernize, and support attribution reporting and index/master data platforms. Develop C#/.NET and Python services, APIs, web components, Azure data pipelines, SQL solutions, and data warehouse models. Provide production support, testing, reconciliation, CI/CD, documentation, and release coordination. Collaborate with investment, performance, attribution, analytics, and reporting stakeholders while ensuring data quality, auditability, reliability, and secure development practices.

Builds and maintains scalable batch and streaming data pipelines, data models, data flows, API integrations, and data products across on-premises and cloud environments. Translates business analytics requirements into technical solutions, including data cleansing, transformation, governance, validation, reporting support, and AI/ML data preparation. Collaborates with business users, data scientists, reporting specialists, and technology teams while executing moderately complex data projects with limited supervision.

Designs, develops, and maintains scalable data pipelines, ETL/ELT solutions, data models, APIs, warehouses, lakehouses, and cloud data platforms. Collaborates with Agile teams, product owners, analysts, architects, and stakeholders to deliver enterprise, analytics, and regulatory data solutions. Implements data quality, governance, lineage, testing, deployment, and automation practices while supporting modernization, migration, production readiness, and continuous platform improvement.

Designs, develops, and maintains scalable data pipelines, ETL/ELT solutions, data models, APIs, warehouses, and lakehouse platforms. Builds solutions using Azure, Databricks, and modern cloud technologies while implementing data quality, governance, lineage, testing, and regulatory controls. Works in Agile feature teams with product owners, analysts, architects, and stakeholders to deliver enterprise data products, reporting solutions, platform modernization, and analytics capabilities. Supports automation, deployment, release activities, defect resolution, and continuous engineering improvement.

Implement a large-scale data platform modernization initiative by migrating approximately 1,000 datasets to Snowflake, upgrading Airflow pipelines, migrating Hive to Iceberg, adapting data pipelines, and supporting workflow monitoring. The role requires hands-on Python, SQL, Spark, Kafka, AWS S3, Snowflake, testing, debugging, and large-scale data migration experience. This is a remote contract engagement through March 2027 for candidates in eligible EMEA and APAC locations.

Supports data onboarding, pipeline monitoring, troubleshooting, maintenance, validation, metadata management, lineage documentation, and cloud platform operations. Develops Python-based ingestion and automation scripts, assists with AWS deployments and monitoring, participates in incident response, and documents operational procedures. Collaborates with senior engineers while working with Amazon S3, AWS Glue Data Catalog, CloudFormation, Databricks, and related data technologies.

Develop and maintain GCP-based ELT pipelines using dbt and BigQuery, migrate SAS transformation logic and datasets to GCP, build SQL data transformations and dimensional models, and perform data validation, reconciliation, and quality testing. The role also involves managing data engineering projects, guiding teams, supporting migration activities, and driving continuous process improvement.

Own end-to-end batch and real-time data pipeline delivery using Databricks, Spark, Python, PySpark, dbt, and Kafka. Design transformation architecture, CI/CD pipelines, DataOps and observability frameworks, and administer Databricks environments. Lead streaming solutions, deployments, migrations, and upgrades while optimizing performance, access, and cost. Partner with stakeholders on requirements and technical trade-offs, mentor engineers, and lead code and design reviews.