Top Data Engineer Jobs in Bengaluru

Build and scale backend APIs, distributed data pipelines, and data platform infrastructure. The role involves lakehouse and warehouse technologies, workflow orchestration, big data frameworks, infrastructure as code, and reliability monitoring. The engineer will design scalable, reliable, cost-efficient systems, collaborate with customers on integrations, and establish engineering best practices for enterprise AI data infrastructure.

Design, build, and operate scalable AWS data pipelines and lakehouse platforms for market data and index computation. Modernize legacy ETL systems, curate financial datasets, develop canonical data models, and implement data quality, governance, lineage, and observability frameworks. Collaborate with product, research, index operations, and engineering teams while monitoring production systems and supporting on-call operations.

Build and maintain batch and streaming data pipelines, backend services, REST APIs, Airflow workflows, Spark jobs, and SQL transformations. Own monitoring, data quality, infrastructure stability, production debugging, documentation, and testing. Contribute to identity graph development for fraud detection and collaborate on data models, schemas, and real-time decisioning systems. The role requires strong programming, SQL, database, distributed systems, cloud, and software engineering fundamentals.

Design and develop scalable Azure ETL/ELT pipelines and modern cloud data platforms. Build Databricks and PySpark solutions, implement Bronze/Silver/Gold Medallion Architecture, develop Airflow and dbt workflows, create analytics data models, and maintain CI/CD, governance, and data quality standards.

Develop and optimize enterprise-scale data engineering solutions using Databricks, ETL/ELT pipelines, cloud platforms, advanced data modeling, SQL, and Python or Scala. Responsibilities include building scalable data platforms, ensuring data quality, optimizing performance, troubleshooting issues, and collaborating with stakeholders.

Design, build, and maintain scalable Snowflake data pipelines and cloud data solutions. Develop ETL/ELT workflows, SQL queries, stored procedures, and data models; integrate APIs, databases, SaaS platforms, and cloud storage. Optimize Snowflake performance, implement data quality monitoring, support reporting and analytics, and translate stakeholder requirements into reliable data solutions.

Develop and maintain scalable data pipelines using Python and PySpark. Build Azure-based ETL and ELT solutions with Data Factory, Data Lake, Blob Storage, Databricks, Synapse, and Azure SQL. Write complex SQL queries, stored procedures, and transformations; troubleshoot pipeline issues; collaborate with technical and business teams; and follow engineering best practices using Git, Azure DevOps, and Jira.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Build and support scalable healthcare data platforms, unified data models, datasets, and ad hoc data access solutions. Extract and integrate heterogeneous healthcare data, improve data quality, support acquisitions, and model data for BI, software applications, machine learning, and AI. Collaborate with product, engineering, business, and clinical stakeholders while following software development lifecycle best practices.

Build and maintain scalable AWS data pipelines, warehouses, schemas, and ingestion systems for large structured and unstructured datasets. Develop ETL/ELT workflows using AWS services including Glue, Lambda, S3, Redshift, and CodePipeline. Create dimensional models, analytics tools, and data marts while ensuring data quality, privacy, compliance, and performance. Collaborate with cross-functional stakeholders to resolve data issues, improve infrastructure, and deliver actionable business insights.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

Develop scalable AWS and Databricks data solutions, including ETL/ELT pipelines, batch and near-real-time processing, data modeling, and large-scale data engineering. Use Spark, PySpark, Spark SQL, Python, Scala, Delta Lake, and AWS storage. Collaborate with architects, BI, and data science teams while applying engineering best practices. Preferred experience includes ML/AI workloads, feature engineering, MLflow, CI/CD, Git, governance, compliance, Agile/Scrum, and insurance.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.

Designs and maintains scalable data pipelines using AWS, Spark, Kafka, Airflow, and related technologies. Collaborates with product, data science, and business teams; develops reporting dashboards; documents data flows and runbooks; optimizes workflows; troubleshoots data processing issues; and supports Agile delivery and CI/CD practices.

Build and manage scalable data pipelines in Databricks, transform raw data with SQL and dbt, and produce reliable data assets. Collaborate across teams to deliver insights for products, AI, and business decisions. Support machine learning research by validating model performance, optimize data processing and reporting, and work with AWS services, Airflow, visualization tools, and databases.

Build scalable telemetry and usage-data pipelines using AWS Glue, PySpark, Python, SQL, and Snowflake. Transform raw data into rated, validated, invoice-ready outputs for SAP billing and usage-based billing systems. Implement rating logic, data quality checks, reconciliation, audit trails, exception handling, monitoring, and CI/CD deployments. Optimize data models and pipelines while supporting batch and event-driven ingestion and collaborating with engineering, finance, billing, and operations teams.

Build and optimize high-throughput market and execution-data pipelines using Python, R, and PostgreSQL. Orchestrate scheduled workflows, maintain 24/7 data availability, monitor pipeline health, implement data-quality checks, and develop REST APIs for analytics. Collaborate with quants and product managers to turn transaction-cost research into production features and client analyses while applying statistical methods to order-flow data.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

Design and implement scalable Lakehouse architectures and ETL/ELT pipelines using Databricks, PySpark, SparkSQL, Delta Lake, and DBT. Optimize SQL and data performance, model data, integrate ERP systems, and support governance, lineage, security, CI/CD, and automated deployments.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Build and maintain scalable data pipelines, BigQuery warehouses, Dataform/dbt transformations, data services, APIs, and data quality monitoring in GCP. Support machine learning workflows and develop an AI-driven insights layer. Collaborate with analysts, data scientists, and stakeholders to deliver reliable data solutions for Easyship’s SaaS and eCommerce shipping platform. This is a full-time onsite role in Bangalore aligned with UK working hours.

Lead enterprise Salesforce Data 360 implementations, including data ingestion, identity resolution, data transforms, calculated insights, segmentation, and activation. Design scalable batch, streaming, API, and Zero Copy integrations while ensuring security, performance, and governance. Translate business needs into technical plans, support testing and go-live, resolve defects, optimize production systems, guide developers, and collaborate with business, marketing, analytics, and IT stakeholders.

Designs and delivers enterprise Salesforce Data 360 solutions, including data ingestion, modeling, identity resolution, transformations, segmentation, activation, and calculated insights. Translates business requirements into scalable technical designs aligned with security and governance standards. Collaborates with business, marketing, analytics, and IT stakeholders while supporting UAT, go-live, defect resolution, and optimization. Provides technical guidance across delivery activities and works with batch, streaming, APIs, integration pipelines, and advanced SQL.