Top Data Engineer Jobs in Bangalore

Build and operate internal data platforms, distributed workflow systems, and developer-facing applications. Develop Python libraries, administer Snowflake, Airflow, dbt Cloud, and Kafka, and own data governance, reliability, observability, and cost optimization. Support analytics engineering through orchestration, CI/CD, testing, and metadata capabilities. Automate service and ML model deployment while partnering with engineering, analytics, operations, business, and product teams.

Build and maintain large-scale Snowflake data platforms and pipelines, including AWS and legacy-platform migrations. Develop SQL, Spark, PySpark, and Python transformations; implement batch and streaming workflows; optimize Snowflake performance; and apply security, governance, and data-sharing capabilities. Support migration validation, troubleshooting, monitoring, production operations, SDLC, CI/CD, and stakeholder collaboration while using Snowflake AI and migration tools.

Develops and manages scalable data pipelines using Snowflake, SQL, dbt, and Airflow. Builds REST APIs and microservices with Python and FastAPI, implements CI/CD pipelines, and designs reusable Slack app frameworks. Collaborates cross-functionally on high-quality solutions and may support Salesforce Data Cloud administration.

Designs, develops, and maintains scalable data pipelines, data architectures, quality processes, and integrations across varied sources. Troubleshoots data issues, monitors solutions to meet SLAs, documents infrastructure, and collaborates with stakeholders and cross-functional teams. Requires strong SQL, database management, dimensional and relational modeling, and Python or Java experience, with preferred expertise in cloud data warehouses, big data, ETL tools, orchestration, BI dashboards, and Agile delivery.

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, 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.

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.

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.

Build, optimize, and maintain scalable ETL/ELT data pipelines and workflows using Python, PySpark, SQL, and cloud services (GCP/AWS/Azure). Ensure data quality, performance tuning, observability, and collaborate with cross-functional teams to deliver governed data products and documentation.

Design, build, and operate Digital Data Platform pipelines and DataOps capabilities. Implement ETL/ELT, streaming, API integrations, data quality, governance, security, and self-service access using Python, SQL and GCP services. Collaborate with stakeholders to document requirements, test and troubleshoot data processes, and enable analytics and BI consumption.

Build and optimize scalable lakehouse infrastructure supporting sub-minute latency and unified batch and streaming workloads. Develop CDC pipelines, distributed query systems, ETL/ELT frameworks, data quality controls, and analytics models. Use AWS, PySpark, Kafka, Iceberg, Airflow, and related technologies to deliver reliable data products. Lead technical initiatives, design reviews, domain modeling, and stakeholder-focused analytics solutions while leveraging AI tools to accelerate development.

Develops and manages scalable data pipelines using Snowflake, SQL, dbt, and Airflow; builds Python-based REST APIs and microservices; supports Salesforce Data Cloud; implements CI/CD pipelines; designs reusable Slack app frameworks; and collaborates cross-functionally on high-quality data solutions.

Designs, builds, and maintains scalable data platforms and pipelines using AWS, Snowflake, dbt, and Databricks. Develops infrastructure-as-code and CI/CD pipelines, supports AI/ML systems with reliable datasets, and implements data governance, security, compliance, lineage, and quality practices. Collaborates with engineers, analysts, scientists, product managers, and designers to deliver end-to-end solutions.

The Lead Data Engineer will design, optimize cloud-based data pipelines, implement data models, ensure data quality, collaborate across teams, and build automated ETL/ELT workflows.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Build and maintain data transformation pipelines, automated QA processes, and AI-assisted tools for debugging, documentation, and transformation. Develop prototype datasets, codify transformation logic, and support adoption of new technologies through mentoring, workshops, and training. Collaborate cross-functionally in an agile environment while applying Python, cloud services, SQL, CI/CD, machine learning, and data visualization tools.

Build and maintain AWS-based ETL pipelines using Glue and PySpark, orchestrate workflows with Step Functions, and manage Iceberg data lake tables. Develop and optimize QuickSight dashboards backed by Athena, monitor data quality and pipeline performance, troubleshoot production issues, and document technical solutions. Collaborate independently with stakeholders and engineering teams across time zones while following code review, Git, and CI/CD practices.

Design, develop, and maintain scalable ETL/ELT pipelines using PySpark and Python on GCP. Build ingestion frameworks, implement transformation and cleansing, optimize Spark jobs, ensure data quality and governance, troubleshoot production issues, and participate in CI/CD and DevOps practices.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Design, build, and optimize end-to-end Databricks ETL/ELT pipelines using Delta Lake, DLT, Auto Loader, PySpark and Spark SQL. Implement medallion lakehouse architecture, governance with Unity Catalog, data quality, CDC, performance optimization, orchestration, CI/CD, monitoring, and deliver production-grade, tested increments for high-volume regulated data workloads.

Designs and maintains scalable data platforms, architectures, ETL pipelines, and complex SQL solutions across multiple databases. Develops Python applications and LLM-powered solutions using LangChain, RAG, and AWS Bedrock. Implements Airflow orchestration, data quality and governance practices, AWS deployments, CI/CD, and production support. Collaborates with data scientists, analysts, product managers, and engineering teams to translate requirements into reliable data-driven solutions and reporting.

Design, build, and optimize scalable batch and streaming data pipelines; manage Snowflake and BigQuery warehouses; develop dbt models; orchestrate workflows across Azure and Google Cloud; ensure data governance, observability, security, and high-quality data products; participate in a light on-call rotation.