Top Data Engineer Jobs in Bengaluru

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 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.

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.

Design, build, and maintain scalable batch and real-time data pipelines, ETL/ELT workflows, data models, warehouses, and lakes. Ensure data quality, reliability, security, and performance while integrating APIs, databases, cloud platforms, and third-party systems. Collaborate with data science, machine learning, engineering, and product teams to provide datasets for analytics and machine learning. Monitor pipelines, troubleshoot infrastructure issues, implement testing and validation frameworks, and contribute to data platform architecture.

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.

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 migration from Matillion ETL to AWS-native data platforms. Design and optimize scalable pipelines using AWS Glue and Apache Airflow, build reusable ETL/ELT components, and work with Snowflake for data warehousing and performance tuning. Implement data quality, monitoring, security, governance, and compliance practices. Collaborate with architects and stakeholders, support CI/CD and DevOps processes, conduct code reviews, mentor engineers, and improve processing performance, cost, and scalability.

Designs, builds, and optimizes scalable data pipelines, ETL processes, data models, APIs, and cloud-based data infrastructure. The role integrates structured and unstructured data, manages databases, warehouses, lakehouses, and distributed systems, and ensures data quality, security, governance, and performance. Responsibilities also include troubleshooting, technical documentation, architectural contribution, cross-functional collaboration, and mentoring team members.

Design, develop, optimize, and maintain scalable ETL pipelines using PySpark and AWS Glue. Build data ingestion and transformation workflows, orchestrate processes with AWS Step Functions, and develop Python-based AWS Lambda functions. Support AWS data lake architectures, analytical use cases, monitoring, reliability, and performance optimization. The role also involves SQL and may include Java microservices, REST APIs, and backend integrations.

Lead the design and development of scalable Azure data pipelines and ETL/ELT solutions using Python, PySpark, SQL, Databricks, Data Factory, Synapse, and related Azure services. Provide technical leadership through code reviews, documentation, troubleshooting, mentoring, and engineering best practices. Collaborate with cross-functional teams to translate business requirements into technical solutions and support data warehousing, lakehouse architecture, and governance initiatives.

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.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Design, develop, and optimize scalable ETL pipelines and distributed data applications for batch and streaming workloads. Manage workflows with Apache Airflow, operate data platforms on AWS and Amazon S3, ensure data quality and performance, troubleshoot production issues, and collaborate with engineering, product, and analytics teams.

Build and own Zinier’s AWS-based medallion data platform, including canonical data models, CDC pipelines, ETL transformations, dimensional schemas, tenant isolation, governance, lineage, and self-service BI. Migrate divergent client schemas into a unified model, enable QuickSight reporting, and establish clean historical data foundations for AI/ML. Collaborate with Product, Solutions, Engineering, Customer Success, and AI teams while balancing scalable architecture with hands-on implementation and troubleshooting.

The AWS Data Engineer will design and implement data pipelines using AWS services, develop ETL processes, and optimize data storage solutions.

Build and maintain scalable ETL/ELT pipelines, data warehouses/lakes, and real-time analytics infrastructure. Ensure data quality, governance, and deliver high-quality datasets for product, analytics, and customer-facing dashboards.

Build and scale end-to-end ETL/ELT pipelines (batch and near-real-time), design ingestion systems, optimize cloud data warehouses, enforce data quality and monitoring, define data models/contracts, enable ML/AI workflows, and operate data infrastructure using Python, PostgreSQL, MongoDB, and AWS.

Owner of company-wide reporting and analytics for Growth, Marketing, Product, Finance and Operations. Build and maintain dashboards, automated reports and data pipelines (dbt), ensure data quality and governance, run growth and product analyses (CAC, ROAS, LTV, retention), support A/B testing and ad-hoc decision support, leverage AI for automation, and coordinate VAs on data tasks.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.