Top Data Engineer Jobs in Bangalore

Lead data engineering projects, optimize large-scale data pipelines and platforms, ensure delivery quality, manage risks, and collaborate with analytics and business leaders to align technical work with business goals.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Design, build, and maintain scalable batch and streaming data platforms and pipelines (Snowflake, Kafka/Pulsar). Develop production-grade Python ETL/ELT, data models, automated deployments, and ensure security, governance, performance, reliability, and GenAI enablement. Partner with analytics and business stakeholders to deliver robust data products.

Designs, builds, and operates scalable AWS-based batch and streaming data pipelines and platforms (Lakehouse and warehouse). Owns architecture, performance, security, and cost optimization; implements transformations (SQL/dbt, PySpark), Kafka ingestion, Airflow orchestration, and CI/CD/Terraform. Partners with product and platform teams, mentors engineers, and drives data modeling, schema evolution, and operational excellence.

Build and maintain accounting data reporting and automation for global accounting teams. Design ETL/BI pipelines, investigate high-volume datasets, perform testing/QA, troubleshoot system configuration with users, and deliver accurate reports under tight deadlines.

Validate data platforms, ETL/ELT pipelines, transformations, and analytical models. Design and run data test plans, execute SQL checks, use Databricks/Delta Lake/Spark for verification, and build automated data tests with Python/PySpark/Pandas. Collaborate with engineers, product owners, and SMEs to find root causes and ensure data quality across delivery.

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

Lead design and implementation of product-level data architecture and scalable ETL/ELT pipelines to support analytics and AI/ML. Define and maintain dimensional models, fact/dimension tables, and data quality monitoring. Partner with product, data science, and platform teams to produce analytics- and AI-ready datasets, drive architectural standards, and mentor data engineers.

Design, build, and maintain scalable cloud-native data pipelines for trade lifecycle processing. Ingest, transform, and normalize high-volume financial data (DTCC, ISO15022), implement MQ/API-based ingestion, optimize SQL Server performance, maintain UTR schemas and trade exception databases, ensure data quality, lineage and governance, and collaborate with reconciliation, settlements, and operations stakeholders.

Deliver and maintain a global big-data processing platform, support cloud migration, write tested high-quality code, build and maintain automation test suites in Azure DevOps, perform manual/load/exploratory testing, collaborate with senior developers, estimate sprint tasks, and mentor junior engineers.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

Design, build, and lead production data and ML pipelines (streaming and batch) using Python and PySpark. Develop ETL, feature engineering, EDA, data quality, and anomaly detection. Use Hadoop/Hive, Pandas, SQL/NoSQL, Airflow/Jenkins and CI/CD tooling. Write tested, maintainable code, collaborate with stakeholders, and mentor teams while communicating technical concepts to technical and non-technical audiences.

Lead development and support of Honeywell's Informatica Product 360 PIM/MDM: oversee inbound/outbound jobs, analyze and implement change requests, perform code migrations and upgrades, build PIM modules and data quality customizations, configure Power BI reports, guide downstream integration, run demos with stakeholders, and contribute to AI automation.

The AWS Data Engineer will work on designing and building cloud-based solutions, utilizing AWS services, and integrating systems for clients. Requires experience with various AWS tools and Java development.

Design, build, and maintain scalable data pipelines using Python and SQL on cloud platforms, ensuring data quality and collaboration with analysts and teams.

The Senior AI Data Engineer architects and manages data solutions, ensuring high quality data for AI products, leading technical projects, and mentoring team members in data engineering practices.

Lead architecture and implementation of a scalable next-generation data platform. Design and build batch, streaming, and near-real-time data pipelines, data models, observability, lineage, and ELT/ETL processes. Drive technical vision, evaluate modern data technologies, optimize cost and performance, and mentor engineering teams while collaborating with data scientists, analysts, and product owners.

Develop and maintain data and ETL solutions for compensation systems. Design, code, unit test, and participate in release cycles; perform database administration tasks, collaborate with teams, and apply modern development and testing methodologies.

Develop and maintain compensation-related data solutions: write and test code, participate in design and code reviews, support database administration, and perform ETL and scripting tasks. Collaborate with senior engineers to implement moderate-complexity infrastructure and software solutions.

Design, build, and operate production-grade batch ELT pipelines and a lakehouse (medallion pattern) in Databricks/Spark; ensure data quality, monitoring, SLAs, and create reusable data products while partnering with engineering and business stakeholders.

Develop, maintain, and optimize Snowflake/dbt data pipelines and transformations for NPS and analytics. Write production SQL and Python ELT scripts, enforce data quality, collaborate with BI engineers, and support AI integration and adoption of AI-native data tooling to deliver reliable executive-level insights.

Ensure accuracy, completeness, and governance of client and legal entity data. Translate business needs into data definitions and models, monitor and remediate data quality issues, coordinate with external data providers, support adoption of firm data platforms, maintain master data playbooks, train users, and track quality metrics to drive automation and process improvements across global teams.

Build, optimize, and maintain scalable ETL/ELT data pipelines and workflows using Python, PySpark, SQL, and cloud services (GCP/AWS/Azure). Ensure data quality, performance tuning, observability, and collaborate with cross-functional teams to deliver governed data products and documentation.

Design, build, and operate Digital Data Platform pipelines and DataOps capabilities. Implement ETL/ELT, streaming, API integrations, data quality, governance, security, and self-service access using Python, SQL and GCP services. Collaborate with stakeholders to document requirements, test and troubleshoot data processes, and enable analytics and BI consumption.

The Lead Data Engineer will design, optimize cloud-based data pipelines, implement data models, ensure data quality, collaborate across teams, and build automated ETL/ELT workflows.