Top Data Engineer Jobs in Hyderabad

Design, develop, and maintain ETL/ELT pipelines using Databricks, Snowflake, Python, Teradata, and Airflow. Build scalable data solutions for reporting, analytics, and AI/ML workloads; perform data validation, analysis, and performance tuning. Collaborate on requirements, implement CI/CD with GitHub, support production deployments and monitoring, and ensure data governance, security, and healthcare regulatory compliance.

Lead data engineering for JPMorganChase’s consumer banking technology team. Design scalable batch and real-time data pipelines, data lake architectures, and end-to-end ingestion-to-analytics solutions using AWS, Spark, Glue, Iceberg, Kafka, and Flink. Build self-healing data operations with agentic AI workflows, establish data architecture standards, and promote secure AI-assisted engineering practices, governance, observability, resiliency, and automated deployment.

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Designs and delivers enterprise Azure data platforms, lakehouses, ETL/ELT pipelines, and streaming solutions using Databricks, Spark, Delta Lake, and related Azure services. Leads engineering delivery, mentors team members, establishes technical standards, and collaborates with stakeholders. Responsibilities include data modeling, performance tuning, governance, security, CI/CD, monitoring, observability, and operational readiness across Agile and DevOps environments.

Design, develop, and maintain data solutions, pipelines, ETL processes, and data integrations. Ensure data quality, improve data processes, collaborate across teams, make technical decisions, and mentor junior engineers. The role requires Databricks expertise and familiarity with Azure Analytics Services, Azure Databricks, and PySpark.

Designs, develops, and maintains data solutions, pipelines, and ETL processes using Databricks and Azure technologies. Ensures data quality, integrates and migrates data across systems, collaborates with multiple teams, provides technical solutions, mentors junior engineers, and improves data processes. The role requires expertise in Databricks, data pipeline architecture, ETL, data integration, and data quality practices.

Designs, develops, and supports enterprise-scale big data platforms and distributed systems for cybersecurity analytics and reporting. Responsibilities include cloud and on-premises platform engineering, CI/CD automation, infrastructure management, monitoring, resiliency, security reviews, vulnerability remediation, and open-source software compliance. The role partners across development, infrastructure, and security teams while improving platform reliability, performance, scalability, and operational efficiency.

Build and operate global transparency data pipelines supporting accurate, timely, and compliant disclosure reporting. Responsibilities include data ingestion, transformation, validation, reconciliation, delivery, quality monitoring, exception investigation, production support, automation, and root-cause analysis. The role collaborates with Legal, Compliance, technology, and regional teams to implement regulatory requirements, support new markets and system changes, and maintain reliable data operations using SQL, Python, Databricks, AWS, and related platforms.

Designs, develops, and supports enterprise-scale big data platforms and distributed systems for cybersecurity analytics and reporting. Responsibilities include cloud and on-premises platform engineering, CI/CD, infrastructure automation, monitoring, resiliency, security reviews, vulnerability remediation, and open-source software compliance. The role collaborates with development, infrastructure, and security teams while managing platform performance, availability, and operational improvements.

Designs and reverse-engineers data models, resolves data integration challenges, and supports data architecture strategy. The role requires advanced SQL, ETL, Snowflake, CI/CD, Python, and AWS expertise, especially S3 and Glue. Responsibilities include translating business needs into scalable data solutions, developing dimensional models such as star and snowflake schemas, and advising on data movement and technology architecture.

Design and support data models, reverse-engineer physical database models, solve data integration challenges, and contribute to data architecture decisions. The role requires advanced SQL, Snowflake, ETL, CI/CD, Python, and AWS data services including S3 and Glue. Responsibilities include translating business needs into scalable data solutions and advising on data movement and technology strategies.

Build and maintain GCP-based data pipelines, data products, and AI/ML applications using services including BigQuery, Vertex AI, Dataflow, and Dataproc. Develop transformations with Python, SQL, and PySpark; implement CI/CD, monitoring, testing, and observability; ensure data quality and reliability; troubleshoot pipeline issues; and support stakeholders with data requirements, analytics, and reporting.

Provides L3 managed services engineering for complex, cross-technology client infrastructures. Responsibilities include resolving advanced incidents, monitoring systems, meeting SLAs, improving and automating operations, managing vendors, supporting projects and disaster recovery, creating knowledge articles, and mentoring L2 and junior engineers. The role requires strong ITIL knowledge, extensive managed services experience, client focus, adaptability, and an IT or computing background.

Designs, develops, tests, implements, and supports scalable data solutions on Microsoft Azure and Fabric. Builds optimized pipelines, data models, incremental and full-load processes, distributed processing workflows, reusable frameworks, and analytics platforms. Responsibilities include SQL performance tuning, PySpark and Delta Lake development, DevOps CI/CD, Power BI dashboard publishing and support, solution security and resilience, and mentoring peers.

Designs, builds, and maintains Azure-based data solutions and ETL pipelines using Azure Data Factory and Databricks. Manages SQL and MySQL databases, develops data models, optimizes queries, supports data governance and quality, and documents architectures and pipelines. Collaborates with data scientists, analysts, and business stakeholders to deliver reliable data for analysis and reporting. The role also involves security compliance and may include real-time processing, multi-cloud technologies, and machine learning concepts.

Leads enterprise data engineering strategy, designs cloud and hybrid data architectures, and develops scalable ETL/ELT pipelines. Drives platform modernization, governance, security, DevOps/DataOps, real-time streaming, and data availability for BI, AI, and analytics. Provides technical mentorship, translates business requirements into data solutions, and develops REST APIs, Azure App Services, and Azure PaaS implementations.

Designs, develops, and maintains data solutions, including data pipelines and ETL processes for generating, collecting, transforming, and migrating data across systems. The role ensures data quality, scalability, and performance using AWS Big Data and distributed computing technologies. Responsibilities include collaborating with cross-functional teams, documenting solutions, contributing to technical discussions, independently solving problems, and mentoring junior team members.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

Lead the technical development of PPM data ingestion, transformation, and curated data products. Define source mappings, event schemas, data-quality controls, time alignment, and recovery processes. Build forecast- and optimization-ready datasets across medallion layers, integrate with models, dashboards, APIs, and optimization engines, and oversee code reviews, performance tuning, reconciliation, deployments, and engineering standards. Work with batch and near-real-time data from operational, market, enterprise, and weather systems.

Design, develop, and operate scalable data pipelines, transformations, models, and cloud data platforms. Build reliable analytics solutions using Microsoft Fabric, Spark, Azure services, and data warehousing technologies. Implement data quality, monitoring, orchestration, CI/CD, metadata management, and operational support practices. Partner with business stakeholders and engineering teams to translate requirements into data products, investigate incidents, improve platform resiliency, and advance enterprise data engineering standards.

Design, build, maintain, and optimize scalable ETL/ELT pipelines and data models using modern cloud data platforms. Transform raw data into reliable analytical data, troubleshoot performance issues, and implement CI/CD and version-control best practices. Collaborate with cross-functional and global teams to translate data requirements into technical solutions. The role requires Python, SQL, dbt, Snowflake, AWS, and Agile development experience, with Matillion, Meltano, and microservices experience preferred.

Leads an offshore data engineering team, serves as the bridge between customers and delivery teams, and designs, develops, and optimizes scalable data pipelines, models, ETL/ELT processes, and data integrations. Responsibilities include data ingestion, transformation, validation, orchestration, quality improvement, cloud data warehousing, and technical design using Google Cloud services, dbt, and Informatica IICS.

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.

Designs, builds, and maintains scalable ETL pipelines and data products across multi-cloud, multi-region environments. Investigates and resolves technical, procedural, and operational data issues while applying disciplined development practices. The role requires expertise in data engineering patterns, architecture, big data, programming, warehousing, orchestration, databases, data modeling, streaming, security, version control, containerization, monitoring, and logging.

Designs, develops, and operates scalable data structures and distributed pipelines for data-powered products and data science. Integrates multiple data sources, builds batch and real-time processing solutions, writes production-quality code, and manages testing and deployment. Collaborates with product managers, software engineers, and data scientists while contributing to coding standards, code reviews, agile ceremonies, and mentorship. Applies data security, governance, privacy, and cloud engineering practices.