Top Data Engineer Jobs in Bangalore

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks and Azure. Ensure data quality, optimize workflows, troubleshoot processing issues, and support data migration across systems. Lead architecture decisions, collaborate with multiple teams, mentor junior engineers, and serve as a subject matter expert.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, governance, performance, and reliability while supporting migration and deployment across systems. Collaborate with cross-functional teams, troubleshoot pipeline issues, document workflows, contribute to technical discussions, and guide junior team members.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration and deployment across systems, optimizes workflows, and troubleshoots data issues. The role also involves making team decisions, collaborating across multiple teams, providing technical solutions, and mentoring junior engineers.

Lead data engineering for enterprise banking platforms, building ETL/ELT pipelines, Oracle and SQL/PLSQL solutions, data warehouses, and risk and regulatory reporting systems. Provide production support, troubleshoot batch and data issues, automate validation and reconciliation, ensure data quality and governance, and collaborate with Risk, Finance, Compliance, and Technology stakeholders. Mentor team members and support regulatory submissions and audit readiness.

Designs and maintains scalable data pipelines and warehousing solutions using Python, Snowflake, AWS, DBT, and Control-M. Integrates web services, APIs, microservices, relational databases, and NoSQL systems while monitoring data quality, performance, and cost. Collaborates with data scientists, analysts, and engineers, contributes to governance standards, and supports machine learning and LLM data pipelines.

Design and implement secure Test Data Management solutions: data masking, provisioning, subsetting, synthetic data generation, and lineage. Build and automate data pipelines and platform components (primarily on Azure/Databricks), enforce governance and privacy (GDPR, PII), collaborate with application/QA/data teams, and support audit/compliance requirements.

Join Airbus as a PySpark Developer to optimize data processing for Airframe Services. Responsibilities include performance tuning, data governance, and CI/CD automation in an Agile environment.

Designs, builds, and maintains data foundations, pipelines, integrations, and ETL/ELT solutions across cloud and enterprise systems. Ensures data quality, develops data models, supports deployment and migration, and collaborates with technical and business stakeholders. The role also drives data strategy implementation, change and incident management, stakeholder reporting, process improvement, community initiatives, and mentoring of junior engineers.

Design, develop, and maintain data foundations, pipelines, integrations, and ETL/ELT solutions across cloud and enterprise systems. Ensure data quality, modeling, accessibility, and reliable deployment while partnering with technical and business stakeholders. Serve as a data subject matter expert, mentor junior engineers, support data strategy implementation, coordinate incident and change management, and communicate findings to leadership.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support data platform architecture, governance, security, performance tuning, and deployment across AWS, Azure, or GCP. Collaborate with data scientists, analysts, architects, and engineers to ensure reliable, secure, and accessible data.

Designs, codes, tests, and manages scalable data acquisition, integration, transformation, and management pipelines using Azure, Databricks, SAP Data Services, and DevOps practices. Builds data structures supporting Data as a Service, develops cloud and edge infrastructure, manages CI/CD pipelines and containerization, and collaborates with analysts, architects, product owners, and technical teams to deliver data-driven solutions.

Designs, develops, and tests scalable data management solutions, including acquisition, integration, and transformation pipelines. Builds data structures and infrastructure across cloud and edge environments using Azure, Databricks, and SAP Data Services. Manages DevOps pipelines, continuous deployment, and containerization. Collaborates with technical leads, analysts, architects, product owners, and business stakeholders to define requirements, deliver high-quality code, and enhance applications.

Designs, builds, governs, and supports scalable analytics and data engineering solutions on Microsoft Fabric and Azure. Responsibilities include developing pipelines, lakehouse and semantic models, real-time solutions, and high-performance SQL, Spark, and DAX workloads. The role also manages platform administration, security, governance, testing, monitoring, incident resolution, optimization, and CI/CD. It partners with stakeholders, facilitates design workshops, supports enterprise architecture, mentors teams, and contributes to project estimation.

The Sr Data Engineer I will provide consulting services for clinical systems, manage projects, and design SQL code for data reporting and analytics while ensuring compliance with industry standards.

Design, build, and maintain scalable ETL/ELT pipelines using Informatica and DBT; optimize PL/SQL for Teradata; migrate and integrate data across Teradata, BigQuery, and Snowflake; automate workflows with Python; collaborate with data scientists and analysts to ensure data quality and delivery.

Build and maintain scalable batch and real-time data pipelines on GCP to support analytics and AI. Develop reusable data products, implement data quality, schema management, and metadata enrichment. Collaborate with architects, analysts, and ML teams to enable AI-ready data platforms, monitor/operate pipelines, and follow engineering best practices.

Design, build, optimize, and maintain scalable ETL/ELT data pipelines and data platform components in an AWS/Snowflake ecosystem. Ensure data quality, governance, and near-real-time availability. Collaborate with and mentor junior engineers, contribute to architecture and data modeling, and implement performance tuning and best practices.

Design, build, and operate scalable batch and real-time data pipelines on GCP. Develop reusable data products, semantic models, and AI-ready datasets. Implement ETL/ELT, streaming, orchestration, monitoring, and governance. Collaborate with architects, analytics, and AI teams, support data modernization and production stability, and follow DevOps and engineering best practices.

Design, develop, and maintain data solutions and ETL pipelines on cloud platforms. Ensure data quality, optimize pipeline performance, document workflows, collaborate with cross-functional teams, troubleshoot integrations, and mentor junior engineers while becoming an SME.

The Senior Software Developer will develop and support Qlik Sense applications, ensure best practices, and improve analytics processes while collaborating with clients and teams.

Designs, develops, and supports large-scale Azure data engineering solutions using Azure Data Factory, Databricks, Synapse, data lakes, SQL, Python, and PySpark. The role requires cloud development, analytics, data platform administration, DevOps, CI/CD, infrastructure automation, and experience supporting multiple databases and Azure services in agile product environments.

Design, build, and operate production-grade batch ELT pipelines and a lakehouse (medallion pattern) in Databricks/Spark; ensure data quality, monitoring, SLAs, and create reusable data products while partnering with engineering and business stakeholders.

Design and implement scalable AWS-based data pipelines and ETL using Python; integrate with APIs and microservices; build data models and data warehouses; optimize storage and processing for cost and performance; monitor data quality and availability; collaborate with data scientists and engineers; enforce data governance and privacy.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.