Top Data Engineer Jobs in Bengaluru

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.

Build and prepare data pipelines: mapping, cleansing, transformation, integration, and wrangling using Python and SQL. Perform feature engineering for structured and unstructured data and support API and web app deployment for data products.

Design, build, and maintain scalable ETL/ELT data pipelines on GCP/BigQuery using Python and Spark. Integrate systems (APIs, Oracle EBS), implement workflow orchestration, ensure data quality and security, monitor performance, and document processes while collaborating with analysts and business teams.

Build and maintain scalable ETL pipelines on GCP, transform large/disconnected datasets, automate and optimize data delivery, support stakeholders, and apply data engineering best practices including testing and release management.

Design, build, and maintain scalable ETL/ELT pipelines and microservices for marketing and product data, ingesting from ad platforms (especially Meta Ads) and event systems. Enable campaign performance tracking, lead funnel analysis, attribution, and revenue reporting. Ensure data quality, observability, governance, and documentation while collaborating with marketing, growth, and analytics to deliver actionable datasets and drive business outcomes.

Design, build, and optimize Snowflake data models and dbt pipelines. Create Power BI reports and paginated RDLs with DAX and row-level security. Manage CI/CD with Azure DevOps, version control with Git, enforce data quality and governance, and optimize warehouse cost and performance while collaborating with business stakeholders.

Data Engineer responsible for building and maintaining ETL pipelines, data processing workflows, and data platforms. Must have strong hands-on experience with Healthcare Revenue Cycle Management and understanding of healthcare data workflows. Role requires hybrid (onsite + offshore) work and immediate availability.

Design, build, and maintain scalable Azure-based data pipelines, lakes, and warehouses (ADF, Databricks, ADLS, Synapse). Implement ETL/ELT for batch and real-time ingestion, ensure data quality, security, and governance, optimize performance and cost, support production pipelines, enable analytics, and implement CI/CD and documentation.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Design, build, test, and deploy ETL/ELT data pipelines and data‑warehouse solutions for AML/compliance domains. Produce technical specifications, coordinate stakeholders, automate data ingestion controls/tests, implement DevOps practices, and support releases while ensuring data quality, lineage, and regulatory requirements are met.

Design, build, and maintain scalable batch and streaming data pipelines and ETL processes. Develop Python/PySpark code, manage databases/data warehouses (Snowflake, PostgreSQL, Aurora), ensure data quality and security, collaborate with analysts, engineers, and stakeholders, and integrate data solutions into the platform architecture.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

Design, build, and maintain scalable Azure-based data pipelines and storage solutions (Data Factory, Databricks, Data Lake). Develop ETL/ELT processes, optimize pipeline performance and cost, ensure data security/compliance, collaborate with stakeholders, and document data architectures. Support BI/reporting via Power BI and apply DevOps/CI-CD practices.

Design, build, and maintain scalable AWS-based data pipelines using Python, Spark, and SQL. Manage data in S3, catalog with Glue Crawlers, query with Athena, and process with EMR. Implement serverless workflows with Lambda, monitor with CloudWatch, integrate alerts via SNS, and support dashboards (QuickSight/Power BI/Tableau) for analytics and reporting.

Design, build, and maintain scalable AWS-based ETL pipelines and data warehouse solutions. Process large datasets with Python and PySpark, use S3 and Glue for storage and processing, write optimized SQL, maintain PostgreSQL data models, ensure data quality and performance, and collaborate with stakeholders to troubleshoot pipeline issues.

Design, build, and maintain scalable Azure-based data pipelines and storage solutions using ADF, Databricks, Synapse, Data Lake, and Cosmos DB. Develop ETL/ELT processes, optimize performance and cost, ensure security and compliance, collaborate with stakeholders, troubleshoot pipelines, and document data workflows. Support BI/visualization (Power BI) and CI/CD/DevOps practices.

Design, build, and optimize scalable PySpark-based ETL pipelines using the Apache ecosystem. Ensure production reliability, performance tuning, data quality, lineage, and integration of structured/unstructured sources. Collaborate with data scientists, analysts, and stakeholders; write maintainable Python code and support CI/CD and version control workflows.

Design, build, and maintain scalable data pipelines, ETL processes, and data models to support analytics and product teams. Ensure data quality and reliability, instrument data workflows, and collaborate with US and global engineering teams to operationalize data infrastructure for SPAN's electrification and home energy solutions.

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.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Design, build, and own reliable, low-latency production data pipelines and APIs for a life-sciences catalog. Reduce technical debt, implement CDC, add observability and automated tests, translate business rules into durable pipeline logic, and manage deployments and incident response.

Develop and maintain Python- and SQL-based analytics, reports, dashboards, and RESTful APIs. Collaborate with Product Management and Professional Services to gather requirements, estimate projects, ensure data accuracy, optimize queries, and deliver customer-facing analytics solutions. Master data models and data warehouse architectures and generalize custom reports where possible.

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, build, and operate Bureau's cloud data lake and streaming/batch pipelines to support fraud detection and identity intelligence. Own orchestration (Airflow), reliability, observability, and cost-efficiency. Collaborate with data science and ML teams on feature stores and model pipelines, implement schema governance and data quality, and apply graph databases to model identity networks.

The Test Data Management Engineer will design, implement, and manage test data solutions for financial applications while ensuring compliance with data privacy regulations. Responsibilities include data analysis, provisioning, and optimizing TDM processes, collaborating with various stakeholders, and enhancing automation workflows.