Top Data Engineer Jobs in Bengaluru

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.

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.

Lead the design and management of scalable, AI-ready data platforms and products. Build batch and real-time pipelines, semantic layers, and data models while ensuring governance, quality, reliability, and SLA adherence. Enable ML and agentic AI capabilities, implement DataOps and CI/CD, optimize cloud costs, and maintain platform stability. Collaborate with stakeholders to translate business needs into sustainable technical solutions.

Design, develop, and maintain scalable data engineering applications using GCP, Spark, Hive, and BigQuery. Translate product requirements into technical specifications, support cloud environment deployment, troubleshoot applications, and collaborate across teams to deliver reliable data solutions.

Design and optimize data platform architecture, models, integrations, and database components with a focus on graph databases. Collaborate with data and integration architects and multiple teams on technical decisions, troubleshoot database performance, provide cross-team solutions, and drive knowledge sharing and continuous improvement.

Design and implement Salesforce Data Cloud solutions, including data streams, data model objects, relationships, identity resolution, match rules, transformations, calculated insights, segmentation, and data activation. Build API- and event-based integration pipelines, support customer engagement tracking, manage Salesforce metadata and security, and provide solution design and technical leadership.

Build and maintain scalable web crawlers to extract, validate, and structure product data from eCommerce sites. Use AI-driven techniques, browser automation, proxies, and APIs to handle dynamic content and anti-bot measures. Monitor performance, clean large datasets, collaborate with Product/Engineering/Data teams, and continuously improve crawling frameworks and automation.

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.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

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.

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.

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.

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.

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 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.

Own the architecture, scalability, governance, and technical direction of Amtech’s cloud data platform, including warehouses, tenant data stores, feature stores, and event pipelines. Lead multi-tenant data architecture, hybrid AI data routing, data security, observability, and reliability standards. Build data foundations for analytics, machine learning, and AI agents, including vector databases and RAG pipelines. Provide technical leadership through design reviews, mentoring, roadmap planning, and cross-functional architecture collaboration.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Design and operate governed, AI-ready data products, semantic layers, and natural-language analytics experiences on Snowflake and/or Databricks. Build ETL/ELT pipelines with dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG applications; implement governance, security, lineage, and metadata controls; optimize platform and LLM costs; and partner with Finance stakeholders on trusted data products.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

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.