Top Data Engineer Jobs in Bengaluru

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.

Run and monitor data jobs on Linux servers, recover failures, configure project workflows, and write PostgreSQL and ClickHouse queries. Develop Python, SQL, Bash, or TypeScript scripts for quality, completion, pay, and delivery checks. Package datasets in cloud storage, track client deliverables, interpret a TypeScript codebase, communicate findings to operations and clients, and identify recurring manual work for platform engineering.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

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 maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

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.

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.

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

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.

Leads and develops scalable data pipelines, APIs, microservices, and ETL/ELT systems for regulatory and health agency datasets. Ensures data quality through automated testing, monitoring, and validation; integrates pipelines with NLP, machine learning, and search services; and supports production incident resolution. Mentors junior and mid-level engineers, establishes technical standards, conducts code reviews, and translates architecture and product requirements into actionable engineering work.

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.

Designs, builds, and maintains scalable data pipelines and cloud-based data solutions using Python, SQL, Snowflake, PySpark, and AWS. Develops data ingestion, transformation, analytics, and integration frameworks for large datasets while improving performance, reliability, and data quality. Collaborates with product, business, and QA teams and applies AI-native tools and workflows for coding, testing, reviews, documentation, and productivity.

Designs, builds, deploys, monitors, and supports production data pipelines and architectures. The role requires advanced Python and SQL, data modeling, transaction and fact table development, workflow orchestration, data quality controls, lineage, governance, and regulated-domain experience. It also involves scalable system design, CI/CD, Git-based development, YAML configuration, and collaboration with enterprise data platforms and tooling.

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 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.

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.

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.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.