Top Data Engineer Jobs in Bangalore

Lead the design and development of scalable data architecture and ETL/ELT pipelines, ensuring AI readiness and high-performance analytics across products.

Design and build data pipelines and Snowflake-based data architectures, enforce AI-led data storage standards, review modeling practices, develop ETL, create dashboards and KPIs, ensure privacy compliance, and collaborate with cross-functional teams using tools like Git, Gradle, Airflow, and Kubernetes.

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.

Lead design and deliver end-to-end cloud data pipelines and resilient architectures, implement testing and monitoring, optimize data processing, support production lifecycle and coach engineers while partnering with product and business stakeholders.

Design and develop scalable Python-based industrial IoT solutions, including backend services, REST APIs, data pipelines, ETL processes, and time-series analytics applications. Build machine-learning and deterministic algorithms, write comprehensive tests, deploy solutions on Azure Functions and Azure Databricks, troubleshoot production issues, and collaborate with global agile teams to deliver reliable digital products.

Designs and maintains cloud-based data infrastructure, pipelines, ETL processes, and data ingestion frameworks using SQL and PySpark. Collaborates with data scientists, analysts, and cross-functional teams to deliver scalable data solutions. Troubleshoots data quality and performance issues, documents workflows, and develops automated testing for data pipelines, APIs, microservices, and data warehouses.

Design, build, and operate end-to-end data pipelines and models on Databricks and AWS Lakehouse. Develop SQL/Python ETL, optimize open table formats, orchestrate workflows, implement CI/CD, deliver dashboards, enforce data governance, and prepare data foundations for AI use cases while partnering with analysts and stakeholders.

Designs, builds, and maintains scalable, reliable data pipelines integrating enterprise customer experience data. The role transforms complex datasets into analysis-ready outputs for surveys, dashboards, analytics, and AI-enabled initiatives. Responsibilities include defining trusted data sources and keys, supporting secure data access, ensuring data quality and auditability, and collaborating with analytics, CX, marketing, product, and external teams.

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.

Builds and supports scalable data ingestion, transformation, and delivery pipelines across on-premises and cloud environments. Develops API-driven integrations using AWS, Salesforce, Oracle, and enterprise systems; manages data lakes and warehouses; optimizes SQL and PySpark workloads; supports data quality, reporting, and AI initiatives; and troubleshoots complex pipeline and integration issues.

Design, develop and maintain scalable data solutions and ETL pipelines. Ensure data quality and performance, oversee pipeline monitoring and troubleshooting, lead team decisions, mentor junior engineers, and collaborate cross-functionally to support data migrations and storage strategies.

Design, develop, and maintain data solutions and ETL pipelines. Ensure data quality and performance, manage data migrations, collaborate across teams, mentor juniors, and lead technical decisions for scalable data workflows.

Design, develop, and maintain data solutions and ETL pipelines on cloud platforms. Ensure data quality, migrate and deploy data across systems, optimize workflows, troubleshoot integrations, and mentor team members.

Design, develop, and maintain data solutions and ETL pipelines using Azure Databricks. Ensure data quality, troubleshoot data issues, mentor and lead team members, coordinate cross-functional efforts, and implement best practices for scalable data architectures and migrations.

Design, develop, and maintain scalable data pipelines and ETL processes using Azure Databricks. Ensure data quality, troubleshoot data issues, lead and mentor a team, collaborate across functions, and implement best practices for data integration and cloud-based data processing.

Design, develop, and maintain scalable data pipelines and ETL processes using Azure Databricks. Ensure data quality, troubleshoot data issues, collaborate cross-functionally, lead and mentor team members, and implement data engineering best practices.

Design, build, and curate high-quality datasets, semantic layers, and ETL pipelines to support self-service analytics, reporting, and AI solutions. Collaborate with stakeholders to enable data discovery, govern access, document standards, and implement AI/ML/LLM-powered agents and predictive models to drive business value and performance.

Lead and manage a data engineering team to design, build, and operate scalable, secure data foundations for a business Value Pool. Own full data engineering lifecycle, enforce engineering standards, drive automation (CI/CD, IaC), optimize platform performance and costs, and collaborate with architects, analysts, and data scientists to deliver production-ready assets for analytics and AI.

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.

Design, develop, test and release Big Data server-side processing and data transformation pipelines on Skywise. Build KPIs and dashboards (Qlik/Skywise), prototype solutions, collaborate with product, support and project teams, and deliver weekly analytics deliverables.

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.

Design, build, and support production-grade Snowflake data pipelines and dbt models. Optimize performance, write SQL and Python ETL/ELT scripts, implement data quality tests, and integrate AI/LLM outputs to support analytics and executive reporting.

Design, develop, and optimize SQL-based ETL and data models for a cloud data warehouse. Implement stored procedures, SCD handling, data cleansing, profiling, and performance tuning. Collaborate with BI and stakeholders, document metadata and processes, and maintain AWS-based ETL/data pipelines.

Design and build scalable Azure-based data models, ETL pipelines, and data architectures. Implement data integration using Azure Data Factory/Databricks, ensure data quality, governance, security, and collaborate with data scientists and DevOps for deployment and CI/CD.