Top Data Engineer Jobs in Bangalore

Design and build the data backbone for a no-code data platform: ingestion, schema management, transformation, validation, orchestration, monitoring, observability and AI-assisted features. Implement scalable backend services (APIs, workers, execution engines) using Python/TypeScript/Go, leverage distributed processing (Spark/Flink), ensure data quality, lineage, and SLA monitoring, and collaborate across product, frontend, and QA while mentoring peers.

Design, build, and maintain scalable, fault-tolerant data platform components. Implement semantic modeling (dbt/dbt MetricFlow), optimize query performance, build ETL/streaming pipelines, ensure observability and data quality, participate in on-call rotations, and collaborate cross-functionally to enable governed data sharing.

Design and build high-performance, real-time data pipelines and infrastructure for agentic AI. Implement, test, document, and deploy components using Python, SQL, AWS, Terraform and CI/CD. Provide estimates, own delivery, debug production issues, and optimize performance and cost while collaborating across teams.

Design, build, and maintain enterprise data platform and ETL/ELT pipelines (file, DB, API) using Snowflake/AWS. Optimize SQL and pipelines, support production incidents, work in Agile with global teams, and ensure data security and governance.

Design, build, and maintain scalable data pipelines, ETL processes, and data architectures (lakes, warehouses). Develop real-time streaming apps (Spark Streaming, Flink), optimize Spark performance, ensure data quality, governance, and security, integrate AI/ML pipelines, evaluate new data technologies, troubleshoot pipeline performance, and mentor junior engineers.

Leads enterprise data engineering, analytics, reporting, regulatory data, and platform initiatives for banking and financial services. Designs data warehouses, lakes, pipelines, integrations, reporting marts, dashboards, and Oracle solutions. Ensures data quality, governance, reconciliation, lineage, and regulatory compliance across lending, risk, securities, tax, payments, treasury, and customer data. Provides L2/L3 production support, resolves incidents, drives performance improvements, and partners with senior stakeholders and global teams.

Leads enterprise data engineering, warehousing, integration, analytics, reporting, and regulatory data initiatives for banking and financial services. Designs scalable pipelines and data platforms, develops Oracle SQL and PL/SQL solutions, builds BI dashboards, manages data quality and governance, supports lending, risk, securities, tax, payments, treasury, and customer data domains, and provides L2/L3 production support. Partners with senior stakeholders and leads global teams to improve reporting accuracy, compliance, reconciliation, and operational performance.

Designs and operates enterprise data governance infrastructure across Snowflake and AWS. Responsibilities include RBAC, authentication, masking, row-level security, compliance automation, Terraform provisioning, access workflows, monitoring, SOX evidence, data sharing, Iceberg governance, orchestration, cost optimization, and Snowflake AI/ML governance. The role also supports platform reliability, self-service data access, and scalable identity integration using Okta and SCIM.

Supports data platform architecture, blueprint development, component design, and integration with systems and data models. Collaborates with Integration and Data Architects, troubleshoots platform integration and performance issues, documents design decisions, contributes solutions, and guides junior team members. Requires expertise in Ab Initio, data integration, data modeling, workflow processing, pipeline orchestration, data quality, governance, and performance optimization.

Designs and supports data platform blueprints, integrating data platform components, systems, and data models. Collaborates with Integration and Data Architects, contributes technical solutions, documents processes, participates in team discussions, and develops expertise in Azure Databricks, data warehousing, ETL, data modeling, and cloud data processing.

Design, build, and maintain dbt models and Snowflake data warehouses, own CI/CD and orchestration for pipelines, implement data quality tests, partner with stakeholders to translate requirements into production-ready data models, and optimize performance and observability.

As a Tech Lead, you will govern data modeling and analytics performance, support engineering teams, ensure scalable integration, and maintain standards for the lakehouse architecture.

Design, develop, and operate data pipelines in Azure Databricks, ensure data quality for HR, Purchasing, and Finance, and implement robust data models and validations.

The IT engineer will design and develop data pipelines in Azure Databricks, ensuring data availability and quality for Manufacturing & Engineering. Responsibilities include collaborative development, implementing complex data models, and applying software engineering practices.

The IT engineer will design and develop scalable data pipelines in Azure Databricks, ensuring data quality and availability for HR, Purchasing, and Finance. Key tasks include implementing ingestion from various sources, developing modular code, and collaborating across teams to refine user stories and align with governance standards.

The IT engineer will design and develop data pipelines in Azure Databricks, ensuring data quality for Sales & Finance. Responsibilities include implementing data architectures, collaborating with stakeholders, and applying agile methodologies.

The Senior Data Engineer role involves data mapping, ETL process development, and collaboration with stakeholders to deliver high-quality data solutions.

Design, build, and optimize scalable data pipelines and enterprise data products to support supply chain, manufacturing, and operations analytics. Implement Snowflake-based ELT, dbt models, Airflow orchestration, data quality/testing, monitoring, and integration with enterprise systems. Partner with stakeholders, mentor junior engineers, support production incidents, and drive platform modernization and self-service analytics.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Set technical direction for the cloud data warehouse and lakehouse; design data models, pipelines, and storage; partner with analytics, product, and AI teams; lead cross-team technical initiatives; improve data quality, governance, and observability; mentor engineers; evaluate tools; and ensure platform scalability for high-volume and AI data.

Design, develop, and optimize ETL pipelines and repositories for large-scale medical imaging (CT, MRI). Integrate with PACS/XNAT, extract and standardize DICOM/HL7/FHIR metadata, implement automated labeling/segmentation, and optimize AWS storage and retrieval for high-performance clinical applications.

The role involves designing and building data systems using various technologies, especially within Microsoft Azure, and requires experience in system integration and data warehousing.

The Staff Data Engineer will lead data infrastructure operations, implement AI automation, and develop analytical tools while mentoring others and engaging with various stakeholders.

Design, build, and configure Snowflake integration, drive client discussions, manage project deliverables, and integrate data from SAP to Kinaxis.

The role involves utilizing advanced technical knowledge to solve challenges while guiding teams and enhancing client solutions. Emphasis is on collaboration and extending beyond traditional coding tasks.