Top Data Engineer Jobs in Bangalore

Design, build, and operate production-grade, metadata-driven data ingestion pipelines and platform components. Implement CDC/delta/event patterns, integrate sources (SAP, Salesforce, SharePoint), manage metadata and data catalog (Unity Catalog), enforce data quality frameworks, and collaborate with cybersecurity, infrastructure, and business stakeholders in a CI/CD-driven environment.

Design, deploy, and monitor data collection pipelines (Adverity). Build ETL/DBT transformations in Google Cloud Platform, implement data quality checks, troubleshoot automated jobs, support dashboards (Google Data Studio/PowerBI), translate business needs to technical specs, mentor junior staff, and manage third-party data provider relationships.

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.

Build and maintain scalable data marts and ETL/ELT pipelines using dbt, Spark, and Delta Lake. Design dimensional models (facts, dimensions, SCD Type 2), implement data quality, monitoring, and observability, optimize performance and cost, and use AI/LLMs to improve development. Collaborate with analysts and stakeholders to translate business needs into reliable analytical solutions and drive platform improvements, governance, and automation.

Design, develop, and operate scalable batch and streaming data pipelines in Azure Databricks using PySpark/Scala. Build lakehouse bronze/silver/gold layers, implement ingestion from SAP/APIs, ensure data quality and MDM, enable reporting via Power BI, apply TDD and CI/CD with version control, optimize performance, and collaborate with global stakeholders in agile product teams.

Design, build, and maintain scalable data integration pipelines and platform infrastructure using Snowflake, Airflow, Starburst, dbt, Kafka, and Fivetran. Ensure data integrity, performance, security, and collaborate with cross-functional teams to deliver high-quality data solutions and analytics support.

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.

Develop, test, and implement data engineering solutions. Analyze datasets, build ETL jobs, and manage data structures for analytics.

Design, build and maintain scalable AWS-based ETL/ELT data pipelines and reusable platform capabilities (ingestion, transformation, orchestration, data quality, cataloguing). Drive automation, CI/CD, IaC, observability, and data governance. Mentor engineers, integrate LLM/AI tools (Claude Code, SageMaker, Bedrock), and collaborate with cross-functional teams to deliver data products and support downstream ML and analytics.

Architect and build scalable batch and streaming data pipelines, trusted datasets, semantic and metric layers, and enterprise data platforms. Own data quality, observability, monitoring, testing, and documentation. Apply dimensional modeling, Data Vault, or One Big Table principles in multi-tenant environments, including multi-region replication. Partner with business, AI, product, and client teams to translate requirements into reliable analytics and AI-querying solutions.

Support Snowflake platform reliability, quality, and governance. Implement and maintain DataOps processes, CI/CD pipelines, automated testing, monitoring, orchestration, and data quality checks. Monitor pipelines, respond to incidents/on-call, assist with schema validation, lineage, access controls, and cost/performance optimization. Maintain documentation, runbooks, and compliance evidence.

Design, develop, and maintain production-scale Amazon QuickSight dashboards. Implement SPICE optimization, DirectQuery, data blending, custom visuals, and row-level security. Validate KPIs, test dashboard functionality, document implementations and test cases, model data and optimize SQL queries and visualizations for performance, and communicate requirements and results with stakeholders.

Lead a team of Data Engineers to modernize cloud infrastructure, migrate data to Google BigQuery, and develop scalable ETL pipelines.

Design, implement, and maintain data pipelines and infrastructure for Roku's experimentation platform. Build orchestrated workflows, develop distributed processing jobs, ensure data quality and observability, support cross-cloud migration and storage modernization, and collaborate with product, analytics, and engineering teams to enable scalable experiment analytics.

The Data Operations Analyst ensures smooth implementation and delivery of data to portfolio managers by supporting data pipelines, validation, cleaning, and documentation. Requires technical expertise in support roles and specific tools.

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.

Test and validate data ingestion, transformation, and load processes across batch and streaming pipelines; verify warehouse/lakehouse schemas, completeness, accuracy, and timeliness; build automated data quality checks and integrate them into CI/CD; reconcile source-to-target data; validate BI reports and AI/ML models; support data migration, regression testing, monitoring, anomaly detection, and compliance-aware testing (CCPA/GDPR/PCI-DSS).

Design, build, and maintain batch and real-time ETL/ELT data pipelines and workflows. Automate ingestion, aggregation and logging; ensure data accuracy, security, and compliance. Implement data warehouse and lake solutions, mentor junior engineers, and collaborate with stakeholders to meet business requirements.

Design, build, and optimize Snowflake-based data warehouses and ETL/ELT pipelines. Develop complex SQL transformations, Python code, DBT models, ensure data quality and governance, integrate diverse data sources, monitor performance and troubleshoot production issues, and collaborate with analysts and architects to deliver reliable analytics solutions.

Design, develop, and maintain ETL/data pipelines using SSIS; manage and optimize MySQL databases and SQL queries; build and deploy SSRS reports; lead data warehousing and integration efforts; troubleshoot data engineering issues and collaborate with stakeholders.

Senior Database/Data Integration Engineer with 5–6 years' PostgreSQL experience. Requires prior employment at a publicly listed company (2+ years). Preferred skills: AWS data services including Redshift, Python, PostgreSQL performance optimization, and familiarity with Splunk.

The Senior Data Engineer will build data pipelines, implement data governance, automate processes, and enhance data tooling to improve insights across teams.

Design, build, and operate large-scale data ingestion, transformation, and processing frameworks and lakehouse architectures. Develop batch and streaming pipelines, data models, and data products for analytics and ML. Collaborate with product, analytics, and engineering teams, enforce security and governance, document data flows, and implement testing and automation for reliable production operations.

Design and scale a Customer Data Platform to unify customer data, ensure data quality and governance, and support real-time personalization and analytics. Architect and implement ingestion pipelines, storage, distributed query engines, ETL/ELT workflows, and integrations while ensuring reliability, observability, and cross-functional collaboration.