Top Data Engineer Jobs in Bangalore

Build, test, optimize, and maintain real-time and batch data pipelines using SQL, PySpark, and Python. Perform data mappings, requirements analysis, and data quality validation across enterprise data platforms, AWS, and data warehouses. Collaborate with engineers, analysts, stakeholders, project managers, and scrum teams using Agile and DevOps practices. Contribute to data platform architecture, roadmap initiatives, BI reporting, and LLM-enabled productivity solutions.

Design, build, and scale AWS-based data pipelines supporting machine learning dataset creation, benchmarking, evaluation, and production workflows. Manage Airflow orchestration, write Python and SQL for large-scale processing, improve pipeline reliability and observability, monitor data quality and drift, troubleshoot data issues, and collaborate with ML, platform, and product teams on scalable data architecture.

Develop and maintain reporting, analytics, and visualizations (Power BI) to support PSI processes. Automate KPI distribution, measure forecast accuracy, analyze deviations, and partner with demand, supply, finance, and sales to enable data-driven supply chain decisions.

Build, deploy, test, debug, and optimize scalable production data pipelines using Python, Spark/PySpark, AWS, Databricks, Snowflake, and modern data platforms. Apply strong software engineering practices while supporting cloud-native data solutions. The role requires approximately five years of hands-on data engineering experience and may benefit from financial services, orchestration, and cloud-native expertise.

Build and evolve a reliable, scalable data platform supporting analytics, credit models, experimentation, and operational reporting. Responsibilities include creating batch and near-real-time ingestion frameworks, data modeling and semantic layers, governance and security controls, lineage, PII handling, observability, and platform roadmaps. The role also involves scaling backend services, partnering with Product and Data Science teams, and supporting large distributed datasets using modern orchestration, warehouse, and programming technologies.

Design, build, and maintain scalable cloud-based data pipelines, models, workflows, and transformations. Own pipeline architecture, orchestration, monitoring, reliability, performance, and data quality. Manage PostgreSQL, Redshift, Airflow, and Terraform-based infrastructure while applying software-engineering practices such as testing, code review, observability, and documentation. Collaborate with globally distributed technical and business teams to improve data literacy and deliver trusted information.

Design and implement scalable data engineering solutions using Cognite Data Fusion. Build integrations, data pipelines, custom data models, and data cleansing workflows with Python, Java, SQL, and REST APIs. Collaborate with data scientists, architects, project managers, engineering, and product teams on customer deliveries. Conduct code reviews, support customer data engineering tasks, contribute to Cognite tools and SDKs, and apply DevOps practices using Kubernetes, cloud services, Git, and CI/CD.

Designs, validates, and productionizes multi-cloud lakehouse and data platform architectures. Builds scalable batch and streaming pipelines using Python, Scala, SQL, and Spark; develops Iceberg data models; improves performance, reliability, governance, and observability; and creates AI-ready data products and services. Leads POCs through production, supports operational readiness, and mentors L2/L3 teams maintaining legacy platforms.

Owns the reliability, support, and enhancement of AI-powered data products such as Data Concierge and AskInsights. Builds and optimizes production-grade pipelines, transformations, and governed data models using dbt, SQL, and Alteryx. Implements testing, monitoring, observability, data-quality controls, integrations, and deployment practices. Partners with engineering, data science, governance, and business teams to deliver secure, scalable enterprise data capabilities supporting analytics, automation, and AI use cases.

Build and maintain analytics data pipelines and transformation layers using Microsoft Fabric, OneLake, Lakehouse, Warehouse, SQL, Python, and PySpark. Integrate data from NetSuite, Salesforce, Rippling, Arena, Informatica, and Celigo. Develop Power BI semantic models and DAX-based reporting using Direct Lake, while supporting data storage in Delta Lake and Parquet formats.

Lead end-to-end enterprise data pipeline development across SAP, BigQuery, and Databricks. Own production incidents, optimize performance, implement data quality checks, and ensure CI/CD testing. Mentor junior engineers, enforce technical standards, collaborate with analysts and data scientists, document reusable patterns, and contribute to the broader data engineering community. The role focuses on scalable lakehouse data products, near-real-time replication, and reliable analytics infrastructure.

Build and maintain cloud data pipelines, transformations, orchestration workflows, governance controls, and analytics-ready datasets. Integrate APIs, Databricks, BI tools, and platform connectors while troubleshooting data quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on architecture, access management, infrastructure migrations, documentation, and production support.

Build and maintain cloud data pipelines, transformation workflows, orchestration, governance, and access controls for analytics products. Integrate APIs, Databricks, BI tools, and multi-tenant data platforms while troubleshooting quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on platform features, architecture documentation, infrastructure migrations, and production support.

Build and optimize Snowflake and dbt data pipelines with SQL and Python. Design enterprise data engineering and AI solutions involving LLMs, RAG, and AI Agents; manage system integrations and Control-M workflows; and deliver secure, scalable production solutions. Collaborate with technical and business stakeholders on platform modernization, data quality, automation, responsible AI, governance, and enterprise adoption.

Build and maintain Python-based data integration pipelines processing millions of data points. Design technical architectures, translate customer requirements into working code, mentor junior engineers, conduct code reviews, oversee production releases, and advise clients on integration requirements and defects. Collaborate with data engineering and product teams to apply AI capabilities and automation tools. The role requires strong communication, technical leadership, and client-facing skills.

Leads the design, development, and optimization of scalable ETL/ELT pipelines and cloud-native data platforms. Establishes data quality and monitoring standards, mentors data engineers, conducts code reviews, and drives improvements in architecture, tooling, automation, scalability, and performance. Collaborates with product, analytics, engineering, and business stakeholders to deliver reliable data solutions supporting analytics, automation, and product innovation.

Design and deliver scalable AWS data platforms, including cloud data lakes, warehouses, batch and streaming pipelines, and SQL- and Python-based transformations. Build reliable, fault-tolerant data workflows using AWS services, Spark, and PySpark. Own solutions from development through production support, including performance tuning, cost optimization, troubleshooting, security, and compliance. Collaborate with architects, clients, product owners, DevOps, QA, and global stakeholders while contributing to technical design reviews and engineering best practices.

Design and develop scalable AWS data platforms, data lakes, warehouses, and batch or streaming pipelines. Build SQL and Python-based transformations using Spark and AWS services, optimize performance and costs, troubleshoot production issues, and ensure security, reliability, and scalability. Own delivery from development through production support while collaborating with architects, DevOps, QA, clients, and business stakeholders. Contribute to technical design reviews, coding standards, documentation, and continuous improvement.

Build and operate Glean’s internal data platform, including ingestion pipelines, BigQuery infrastructure, transformations, governance, quality controls, observability, and access management. Partner with internal teams to create reusable, reliable, cost-efficient data capabilities. The role requires hands-on production data engineering, platform reliability, stakeholder collaboration, and operational ownership in a hybrid Bangalore environment.

Lead scalable data engineering solutions using Cognite Data Fusion, including integrations, extraction, data modeling, cleansing, analysis, and production-grade pipelines. Collaborate with data scientists, architects, project managers, engineering, product teams, customers, and partners. Conduct code reviews, apply DevOps and CI/CD practices, support Cognite tools and SDKs, and translate customer needs into product offerings.

Lead vehicle data mining and analytics across cloud and high-performance computing environments. Analyze CAN and Ethernet signals, logs, and anomalies; support root-cause analysis and ADAS machine-learning validation; assist with embedded debugging, vehicle instrumentation, testing, and data collection; and improve analytics workflows while collaborating with algorithm, systems, validation, and project teams.

Design and build scalable data pipelines, APIs, cloud infrastructure, and AI-ready data products for proprietary investment research. Develop distributed data systems, implement data quality and governance controls, optimize performance and reliability, and deploy LLM-based tools and agentic workflows. Partner with investment professionals, researchers, data scientists, and compliance teams while providing technical leadership and guiding architecture and development efforts.

Manage Microsoft Fabric governance, security, access controls, capacity, job monitoring, compliance, and workspace hygiene. Implement data policies across Fabric and Azure, automate administrative and security tasks with APIs, scripts, and notebooks, and create operational dashboards, alerts, and compliance reports. Requires enterprise-scale data governance experience and hands-on expertise with Fabric administration, OneLake, Purview, Power BI, and security controls.

Design, build, and optimize scalable batch and streaming data pipelines on GCP and AWS. Develop ETL/ELT frameworks using SQL, Python, and Apache Beam; optimize BigQuery performance and costs; implement data quality, validation, monitoring, governance, lineage, and metadata tracking; and automate deployments through CI/CD. Collaborate with analytics, product, engineering, and business teams to deliver reliable data solutions for reporting and advanced analytics.

Develop and maintain Snowflake and dbt data transformation pipelines, including models, testing, macros, deployment, validation, and troubleshooting. Orchestrate pipelines with Apache Airflow and ingest data using Fivetran or similar ELT platforms. Apply Python and Cortex AI, with optional PySpark, Terraform, cloud data platforms, product analytics, semantic layers, and AI-assisted development tools.