Top Data Engineer Jobs in Bangalore

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.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

Build and operate scalable cloud data platforms, pipelines, and data products. Partner with BI and domain teams to deliver analytics-ready datasets, ensure data quality and governance, implement monitoring/CI-CD, optimize cost and performance, and document operational runbooks.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

Build and operate Kafka-based streaming applications and develop Spark/Flink data processing jobs. Work with data lake technologies (Apache Iceberg), tune jobs for performance and scalability, write testable code, and collaborate on a big-data analytics platform.

The Data Engineer leads the design and implementation of data architecture and platforms while managing stakeholders and guiding teams to deliver scalable data solutions aligned with business goals.

Senior Data Engineer role supporting WPP Media's AI-driven media platform: design, build and maintain scalable data pipelines and platforms, ensure data quality, and collaborate with data scientists, engineers, and media teams to integrate data with WPP Open.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

Lead and execute end-to-end production data pipelines using Databricks and BigQuery. Set technical standards, design medallion-layered architectures, model facts/dimensions, optimize BigQuery workloads, mentor engineers, and collaborate with analytics, data science, and business stakeholders to deliver scalable, governed data solutions.

The Data Engineer will design and maintain reporting systems, implement AI solutions, develop data pipelines, and support stakeholder reporting needs. The role involves advanced data analysis and ensuring data quality and governance.

Design and maintain data pipelines and dashboards, optimize SQL queries, ensure data quality, and collaborate with stakeholders to support data-driven decisions.

The Data Engineer role requires expertise in DevOps and Data Engineering technologies, particularly in Kubernetes, Terraform, Azure, and building data pipelines. Responsibilities include provisioning data platforms, managing CI/CD pipelines, and implementing IaaS architecture.

The role involves leading the design and implementation of scalable data architecture and platforms, guiding teams in alignment with business objectives and driving data strategy and governance.

Build and maintain data pipelines on a modern stack (StarRocks, Hudi, Trino, Spark), support in-product data features and AI-ready datasets, implement access controls, troubleshoot data quality, and document work while collaborating with senior engineers.

Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.

Leads end-to-end data products from ingestion and transformation through semantic modeling, Power BI reporting, documentation, governance, and user adoption. Builds Microsoft Fabric pipelines, Lakehouse or Warehouse architectures, dimensional models, and secure, optimized Power BI solutions. Analyzes data, supports advanced analytics and AI-assisted reporting, establishes quality and CI/CD practices, collaborates with cross-functional stakeholders, and manages data engineers through objectives, reviews, and development.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

Own and architect the Databricks Lakehouse platform (Unity Catalog, Delta Lake, Spark). Build and govern scalable medallion pipelines, optimize Spark performance and costs, embed IaC and CI/CD, lead migrations, mentor engineers, and drive adoption of AI-ready datasets and AI-assisted tooling across data teams.

Design, build, and maintain scalable ETL/ELT pipelines in GCP (BigQuery, Dataform, Airflow) to integrate data from systems (Salesforce, NetSuite, APIs), build AI agents to automate finance workflows, support production month-end jobs in PST, manage IaC with Terraform, maintain CI/CD in GitLab, and produce documentation and BI/ML support for finance stakeholders.

Designs and maintains SAP BW/4HANA data models and pipelines, develops end-to-end BI solutions, integrates SAP and non-SAP data, and delivers structured datasets for reporting and analytics. The role includes ETL, data transformation, performance optimization, query and load monitoring, and collaboration with business stakeholders. Candidates need strong SQL, data warehousing, SAP Datasphere, SAP BW on HANA, and SAP BDC experience. Power BI, Business Objects, and ABAP are beneficial.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.