Top Data Engineer Jobs in Hyderabad

The Senior Data Migration Developer will manage Workday data migrations, collaborating with clients to design solutions, ensure data quality, and support cutover execution.

Develop, enhance, and support ETL/data pipelines using Sparkola, PySpark and SQL. Build and manage batch and ad-hoc pipelines via Sparkola Job Server, use Cloudera Machine Learning for development, validate datasets for downstream analytics (QlikSense/Superset), engage stakeholders to translate requirements, support production issues, and follow JIRA-driven delivery and governance controls.

Design, build, and optimize end-to-end ETL/ELT data pipelines on an Azure-based platform. Translate business needs into data models, KPIs, and dashboards (Power BI/Tableau). Ensure data quality, documentation, metadata/lineage, and governance while collaborating across engineering, analytics, and data governance teams in an agile environment.

Build and maintain Snowflake and SQL Server data pipelines for an operational data store, including ingestion, transformation, schema modeling, migrations, CI/CD, data quality testing, and legacy batch-to-API modernization. Collaborate with domain teams and architects to support customer, loan, payment, and interaction data. Ensure data accuracy, lineage, observability, and performance while applying generative AI tools to accelerate development and testing.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

Design, build, and optimize scalable Azure cloud data platforms and high-performance ETL pipelines using Synapse, ADF, Spark, SQL, and Python. Implement data models, DataOps/CI-CD, quality checks, cost and performance optimizations, troubleshoot production issues, and collaborate with clients and stakeholders while mentoring junior engineers.

Designs and operates end-to-end data products on Databricks, including ingestion, transformation, streaming pipelines, APIs, applications, and curated datasets. Partners with stakeholders to clarify needs, prototype solutions, and deliver secure, scalable production systems. Responsibilities include data governance, testing, CI/CD, monitoring, optimization, troubleshooting, technical design, code reviews, and mentoring. The role requires strong SQL, Python, PySpark, cloud, event-streaming, backend, and data-modeling expertise.

Designs, builds, and maintains ETL/ELT pipelines feeding Microsoft Fabric Lakehouse platforms. Responsibilities include data ingestion, bronze/silver/gold layer development, CDC, incremental loading, data quality, metadata governance, performance tuning, monitoring, automation, and low-level design documentation. The role collaborates with architects, data modelers, and business analysts to deliver reliable, reusable datasets and promote self-service analytics while applying security, resilience, compliance, and cost-optimization standards.

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

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.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Senior technical leader responsible for designing, building, and scaling cloud-native data platforms and pipelines. Partners with stakeholders, mentors engineers, enforces engineering best practices, ensures data modeling, governance, and quality, debugs production issues, and contributes to architecture, design, and Agile delivery to support business outcomes.

Lead design, build, and optimize scalable data integrations and platforms using OIC and GCP. Develop ETL/ELT pipelines, manage APIs, ensure data quality, troubleshoot integrations, collaborate with stakeholders, document solutions, and apply best practices for secure, analytics-ready data.

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.

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

Design, build, and maintain cloud-based data platform components handling large-scale banking data. Translate requirements into scalable production solutions, ensure performance, uptime, and scalability, own development lifecycle, collaborate with cross-functional teams, and implement infrastructure as code and CI/CD practices on GCP.

Design and implement end-to-end enterprise data architectures; build, maintain, and optimize data pipelines with Python, SQL, and Spark; manage large-scale storage (Iceberg, Hadoop, HDFS); develop dbt models; implement ingestion from APIs and on-prem systems; collaborate with stakeholders and document processes to improve data quality and engineering practices.

Leads a team of data engineers and owns the architecture, implementation, governance, security, and optimization of a GCP Data Lakehouse platform. Responsibilities include designing Spark and Delta Lake pipelines, managing Unity Catalog, improving SQL and ETL performance, enabling analytics and machine learning use cases, implementing CI/CD with GitLab and Jenkins, and communicating technical strategies to stakeholders.

The Senior Data Engineer will design and build data infrastructure and pipelines, enhance AI capabilities, and collaborate with teams on data solutions while mentoring junior engineers.

The Data Architect will design and implement data infrastructure, collaborating with teams to enhance data usage for analytics, automation, and AI, while ensuring compliance and governance.

Design, develop, monitor, and support enterprise ETL/ELT pipelines and data marts. Build data models, STTMs, and technical documentation; analyze source systems (DB2, Oracle), tune SQL/PLSQL, resolve production issues, implement data quality and governance, support cloud modernization (AWS/Azure), and collaborate with business partners and data scientists to deliver scalable data products.

Design, build, and maintain end-to-end data pipelines and ETL/ELT processes on Databricks (Azure). Implement Spark (PySpark/Scala) transformations, manage Delta Lake/Unity Catalog, integrate varied data sources (APIs, databases, IoT), and support analytics via APIs and data services. Ensure data quality, security, CI/CD, and performance optimization while collaborating with cross-functional teams to deliver scalable BI and reporting solutions.

Design, build, and deploy end-to-end data pipelines and ETL/ELT solutions on Databricks. Integrate diverse data sources, implement Spark (PySpark/Scala) transformations, optimize SQL queries, maintain data models, build APIs/data services, ensure data quality/security, monitor/optimize pipeline performance in cloud environments, and follow CI/CD and governance practices.

Lead design and development of scalable cloud data pipelines and ETL/ELT processes using BigQuery, DBT, Airflow, Python, and SQL. Build analytics-ready datasets, implement data quality checks, optimize performance, mentor junior engineers, and collaborate with cross-functional teams to deliver enterprise-grade data solutions.

Designs and operates scalable data and machine learning platforms using Databricks, Python, SQL, Spark, and PySpark. Builds batch, streaming, and production ML pipelines; manages Delta Lake, Unity Catalog, Workflows, MLflow, governance, data quality, observability, and model deployment. Partners with data scientists and business teams, optimizes performance and costs, contributes to platform architecture, mentors engineers, and documents operational practices.