Top Data Engineer Jobs in Bangalore

Audits data pipelines, optimization outputs, and promotional recommendations for anomalies, data drift, integrity issues, and commercial feasibility. Performs root-cause analysis, scenario modeling, and pre- and post-event evaluations of promotional ROI, cannibalization, halo effects, and trade-spend efficiency. Collaborates with engineering, data science, optimization, UI/UX, and client teams to improve data feeds, model logic, and RGM tool adoption.

Design, build, and optimize large-scale batch and real-time data pipelines using Python, SQL, Spark, PySpark, and Databricks. Manage lakehouse environments, Delta Lake, Unity Catalog, cloud migrations, CI/CD automation, infrastructure provisioning, and containerized processing. Develop data APIs and collaborate with stakeholders to create technical architectures and insights. Mentor team members and support data platform projects in an Agile environment.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Designs, implements, and maintains cloud-based data pipelines and scalable data platform architectures. Integrates disparate data sources while ensuring data quality, integrity, and timeliness. Evaluates Azure infrastructure and services, and designs data lakes, warehouses, and marts for analytics. Develops data processing and transformation workflows using Azure Spark and Azure SQL Pools. Preferred capabilities include infrastructure as code, DevOps and CI/CD, AI-assisted development, generative AI solutions, and lightweight Python applications or APIs.

Designs and maintains scalable Azure-based data platforms, batch and real-time pipelines, data lakes, warehouses, and lakehouse architectures. Develops ETL/ELT workflows, data models, quality frameworks, and secure integrations supporting analytics, reporting, and AI/ML. Optimizes data performance and reliability while implementing governance, monitoring, CI/CD, and DevOps practices. Collaborates with technical and business teams, contributes to architecture and code reviews, and mentors team members.

Designs, develops, tests, implements, and supports scalable data solutions on Microsoft Azure and Fabric. Builds optimized pipelines, data models, incremental and full-load processes, distributed processing workflows, reusable frameworks, and analytics platforms. Responsibilities include SQL performance tuning, PySpark and Delta Lake development, DevOps CI/CD, Power BI dashboard publishing and support, solution security and resilience, and mentoring peers.

Design and deliver scalable Microsoft Fabric data and AI solutions, including Lakehouse architectures, ingestion pipelines, medallion layers, semantic models, DAX measures, AI Agents, Data Agents, ontologies, and RAG experiences. Develop production transformations with Python, PySpark, and SQL; integrate AI capabilities into applications and APIs; and collaborate across technical and business teams. The role also emphasizes governance, testing, documentation, DevOps automation, security, and responsible AI practices.

Designs, develops, and maintains Snowflake and AWS data pipelines, ETL/ELT solutions, data models, and cloud data architecture. Implements CI/CD, DevOps automation, infrastructure as code, monitoring, security, governance, and cost optimization. Collaborates with architects, analysts, and business stakeholders to deliver scalable data solutions.

Design and maintain Snowflake and AWS data pipelines, ETL/ELT solutions, data models, and cloud data architectures. Implement CI/CD, DevOps automation, Terraform-based infrastructure, monitoring, governance, security, and cost optimization. Collaborate with architects, analysts, and stakeholders to deliver scalable data solutions using Snowflake, Spark, dbt, Airflow, and AWS services.

Designs, implements, and optimizes SQL-based data pipelines and ETL processes. Supports data warehousing, data architecture, governance, visualization, cloud cost optimization, streaming, and big data initiatives. Develops dashboards and data models, monitors pipeline performance, collaborates with stakeholders, and communicates technical concepts to nontechnical audiences.

Leads data engineering and technical consulting for clinical systems, including platform configuration, ETL, data warehouse development, custom analytics, reporting, integrations, validation, quality control, and SOP development. Provides technical escalation, solution design, project oversight, mentoring, training, process improvement, and client support while ensuring compliance with eClinical and industry standards.

Designs, develops, deploys, and monitors Databricks data pipelines on AWS using PySpark and SQL. Builds scalable data solutions, quality dashboards, lineage and monitoring tools, and supports legacy data migration. Ensures data security, retention, privacy, governance, and auditability while optimizing processes through automation and code improvements.

Build scalable data engineering and business intelligence solutions using Microsoft Fabric and Power BI. Develop ETL/ELT pipelines, Lakehouse platforms, medallion data layers, data marts, dimensional models, semantic models, dashboards, DAX measures, and KPIs. Implement data quality, governance, monitoring, performance optimization, and security practices while translating stakeholder requirements into trusted analytical datasets and interactive reports.

Build and manage scalable ETL pipelines, data models, Airflow workflows, and dbt transformations for Zoom’s marketing analytics infrastructure. Develop complex SQL and Python solutions, implement data quality monitoring, and collaborate with analysts and marketing stakeholders. The role supports campaign performance, attribution modeling, customer journey analytics, and reliable reporting through production-grade AWS data solutions.

Designs and maintains HR data infrastructure for People Analytics. Builds ETL pipelines, data models, Airflow workflows, dbt transformations, SQL queries, and data quality monitoring systems. Partners with analysts and stakeholders to deliver reliable, scalable datasets and workforce insights. Requires strong SQL, Python, AWS, Airflow, and dbt expertise, plus experience translating business requirements into production-grade data solutions.

Performs vehicle data mining, exploratory analysis, event triage, and root-cause analysis using Python and data-science tools. Supports AI/ML validation for ADAS features, including dataset assessment, labeling, metrics review, and data-quality investigation. Analyzes CAN, Ethernet, ECU logs, and vehicle signals; assists with embedded debugging, instrumentation, vehicle testing, and data collection. Collaborates with algorithm, systems, validation, and engineering teams while following ADAS development and documentation processes.

Designs and optimizes SQL-based data pipelines, ETL processes, data warehouses, and visualization solutions. Supports data governance, security, cloud cost optimization, data architecture, streaming, and big data integration. Collaborates with stakeholders to deliver dashboards and data solutions aligned with business needs while monitoring pipeline performance and ensuring data quality.

Build and migrate governed dbt models from legacy Gold-layer pricing logic into the Silver layer. Reverse-engineer SQL and spreadsheet calculations, validate parity, apply data-quality tests, document lineage and business rules, collaborate with pricing analysts, and manage approved legacy logic cutover.

Lead data engineering initiatives by designing and optimizing reliable data pipelines and analytics solutions. Collaborate with stakeholders, internal engineers, and external partners to prioritize projects and deliver data products. Participate in technical design reviews, improve data quality, lineage, governance, documentation, and workflows, and promote best practices across a global data engineering environment. Requires hands-on experience with Python, SQL, dbt, cloud platforms, CI/CD, and DataOps.

Design, build, maintain, and document data transformation pipelines, models, and processes that produce business-ready datasets. Collaborate with stakeholders to understand data requirements and support business needs. Build distributed systems in cloud environments and use orchestration, infrastructure-as-code, CI/CD, and AI tools to accelerate workflows.

Leads the architecture and implementation of scalable batch and streaming data pipelines using distributed computing. Improves data quality, reliability, monitoring, alerting, and enterprise data governance. Integrates diverse data sources and storage systems, develops fault-tolerant solutions, and oversees data infrastructure operations. The role also leads projects, mentors engineers, collaborates globally, and drives software development and coding best practices.

Designs, develops, and governs scalable data architectures, pipelines, dimensional models, data warehouses, and migration solutions. The role implements data governance, quality profiling, access controls, and security practices while partnering with business and technical teams to address reporting and infrastructure needs. Responsibilities include technology evaluation, automation, code reviews, performance measurement, documentation, and process improvement within a globally distributed Agile/Scrum team.

Design, develop, test, deploy, and support scalable data pipelines, data models, ingestion frameworks, and foundational data platform systems using AWS, Databricks, PySpark, and SQL. Create technical documentation, data lineage, data dictionaries, and metadata management solutions while supporting AWS Lakehouse and platform engineering activities. Collaborate with global teams and stay current with data engineering technologies and best practices.

Build, test, optimize, and maintain batch and real-time data pipelines across AWS or Azure platforms. Perform data mapping, requirements analysis, data quality management, and architecture planning while collaborating with engineering, analytics, project management, and global stakeholders. Apply Agile, DevOps, DataOps, and DevSecOps practices, develop efficient PySpark solutions, and explore LLM and generative AI applications for data engineering productivity.

Build, test, optimize, and maintain batch and real-time data pipelines on enterprise data platforms. Perform data mapping, requirements analysis, data quality management, and stakeholder collaboration. Support AWS or Azure cloud BI solutions, data warehouse reporting, platform architecture, agile delivery, DevOps practices, and roadmap initiatives. Apply PySpark, Python, SQL, and emerging technologies such as large language models and retrieval-augmented generation.