Top Data Engineer Jobs in Bangalore

Designs and maintains scalable data pipelines and models using AWS and Databricks for manufacturing data across global production sites. Enables process trending, modeling, real-time and predictive analytics in a regulated GxP environment. Collaborates with analytics and business teams to improve dashboards and business intelligence, troubleshoots technical issues, supports digital platforms, and follows quality, software development, and project lifecycle standards.

Designs and operates scalable batch and streaming data platforms supporting analytics, machine learning, and generative AI. Builds ingestion, ETL/ELT, orchestration, data quality, observability, governance, and lakehouse solutions. Partners with stakeholders on technical roadmaps, enables AI/ML workflows, improves platform scalability and cost efficiency, reviews engineering work, and mentors engineers.

Design, build, and operate end-to-end cloud data pipeline solutions with a focus on reliability, scalability, security, and zero-downtime releases. Monitor and optimize performance, resolve incidents, support production lifecycle, and collaborate with product teams to deliver data products for finance, payments, and regulatory reporting.

Develops secure, scalable software and data solutions within an agile banking technology team. Responsibilities include designing and coding production applications, troubleshooting systems, creating architecture artifacts, using AI-assisted development tools responsibly, analyzing large datasets, building visualizations and reports, and improving system architecture and coding practices. The role requires software development, system design, testing, operational stability, SDLC, CI/CD, resiliency, and security expertise.

Build and optimize scalable data engineering pipelines, feature stores, data models, and workflows for structured, semi-structured, and unstructured data. Integrate diverse data sources using AWS and Snowflake, implement transformations and quality checks, and support data governance, security, and management. The role also involves web crawling, API and stream-based extraction, workflow automation, root-cause analysis, and collaboration with cross-functional stakeholders in a hybrid HPE office environment.

Designs and owns production ETL/ELT pipelines, medallion architectures, data transformations, validation, monitoring, lineage, and orchestration. Builds GenAI and RAG services, REST APIs, and scalable backend capabilities using Spark and Databricks. Improves retrieval quality, system reliability, observability, data modeling, and performance while contributing to architecture decisions and frontend integrations.

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Build and operate data platform infrastructure supporting analytics, machine learning, and AI. Responsibilities include Lakehouse and Spark systems, distributed processing, data quality, governance, lineage, security, observability, reliability, deployment automation, and platform tooling. Evaluate query and OLAP technologies, improve cost and performance, contribute to open-source projects, partner with data teams, and mentor engineers.

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.

Design, develop, and optimize scalable cloud data pipelines and Snowflake warehouses using AWS, Airflow, dbt, and SQL. Automate data workflows, infrastructure provisioning, deployments, migrations, and operational support. Monitor pipeline health, troubleshoot data quality and performance issues, and maintain secure, reliable data architectures. Collaborate with data scientists, analysts, and enterprise stakeholders on ingestion, transformation, integration, and analytics initiatives while documenting systems and procedures.

Design and build software applications, primarily using PL/SQL and Oracle RDBMS. Apply expertise in sales application portfolios, with preferred experience in sales crediting, compensation, booking, and planning. Work with Java, Informatica, NoSQL, and big-data technologies while following Agile and Waterfall methodologies. Use Jira, Confluence, Bitbucket, Jenkins, and Git, collaborate across teams, navigate ambiguity, and contribute to advanced analytics and AI/ML planning solutions.

Build and operate end-to-end data pipelines, models, dashboards, and analytics on AWS and Databricks. Develop scalable SQL and Python solutions using Spark, lakehouse table formats, Airflow, and Databricks Workflows. Implement data governance, quality, metadata, and CI/CD practices. Partner with business stakeholders to translate questions into data solutions and support AI initiatives, including RAG and agentic use cases.

Designs, develops, and maintains data solutions, pipelines, and ETL processes across systems. Ensures data quality, supports data integration and migration, troubleshoots issues, and optimizes workflows. Collaborates with multiple teams, makes technical decisions, provides solutions, mentors junior members, and improves data processes. Requires AWS Architecture and AWS Glue expertise, along with experience in data modeling, database design, cloud services, data governance, and data quality.

Develop and operate Webex’s big data platform and analytics pipelines. Build Kafka-based streaming applications for ingestion, filtering, enrichment, and replication; develop Spark and Flink processing jobs; manage analytical datasets with Iceberg; and optimize performance, scalability, and cost. Write clean, testable code, participate in code reviews, and collaborate with product and engineering teams. The role also involves working with query engines, SQL, data modeling, and open-source data technologies.

Designs, builds, operationalizes, and maintains scalable GCP data pipelines and warehouses. Responsibilities include ETL optimization, data modeling, governance, lineage, quality checks, monitoring, alerting, documentation, and troubleshooting. The role uses Python, SQL, BigQuery, dbt, Airflow, Dataflow, and related tools to integrate data from multiple sources and support analytics and reporting. It also requires collaboration with stakeholders, reusable software components, and ownership of production data pipelines.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, optimize workflows, support data migration across systems, document processes, collaborate with stakeholders, and contribute technical expertise while mentoring junior team members.

Lead the architecture and development of scalable ETL/ELT pipelines, Snowflake data platforms, dbt models, and reusable semantic layers for analytics, AI, dashboards, and agentic solutions. Translate complex business requirements into resilient data architectures, integrate diverse source systems, establish data quality, governance, observability, lineage, and CI/CD standards, and mentor junior engineers. Collaborate with architects, analysts, engineering teams, and business stakeholders to deliver maintainable, enterprise-scale data solutions.

Build and maintain scalable ETL/ELT pipelines, analytics-ready data models, semantic layers, and consumption solutions using Snowflake and dbt. Integrate structured and unstructured data, support AI and agentic applications, and enforce data quality, governance, lineage, observability, security, and CI/CD standards. Collaborate with architects, analysts, and business stakeholders to translate requirements into reliable data solutions.

Own and optimize large-scale data pipelines, models, analytics infrastructure, and production-critical workflows supporting Content Operations and Site Quality. Build solutions using SQL Server, Iceberg, PySpark, ECS, and Airflow; troubleshoot data quality and performance issues; and support reliable analytics datasets. Partner with stakeholders on technical solutions and roadmaps. Preferred work includes developing AI/LLM, RAG, agent-based, chatbot, and Adobe Analytics solutions.

Own and optimize large-scale data pipelines, models, analytics infrastructure, and production data assets supporting Content Operations and Site Quality. Build solutions with SQL, PySpark, SQL Server, Iceberg, ECS, and Airflow; troubleshoot quality and performance issues; and support reliable analytics datasets. Contribute to AI/LLM, RAG, chatbot, and agent-based solutions integrating Adobe Analytics requirements into SQL. Partner with stakeholders to design scalable solutions and communicate technical concepts clearly.

Develop and maintain cloud-native ELT data pipelines using Snowflake, S3 Iceberg, dbt, Airflow, and Python. Build workflow orchestration, automated testing, CI/CD pipelines, monitoring, data-quality checks, and documentation. Support incremental loads, backfills, failure recovery, deployment across environments, and troubleshooting of data workflows. Collaborate with senior engineers while using AI-assisted development tools to improve productivity and engineering practices.

Designs and optimizes scalable data ingestion pipelines for product telemetry and enterprise business data. Builds automated pipelines using Fivetran, Python, APIs, S3 lakehouse architectures, Kafka, Spark, and Snowflake. Applies advanced SQL, incremental loading, CDC, micro-batching, and rate-limit handling to support reliable, analytics-ready datasets. Implements CI/CD and DevOps practices, monitors pipeline performance, and partners with analysts and platform teams to rapidly deliver data solutions.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes. Ensures data quality, governance, performance, and reliability across systems. The role includes troubleshooting data workflows, documenting processes, collaborating with cross-functional teams, contributing solutions, and mentoring junior team members. Requires Databricks expertise and familiarity with cloud-based data storage and processing.

Design, develop, and maintain enterprise data solutions, pipelines, and ETL processes. Ensure data quality, optimize workflows, and support data migration across systems. Lead AI-driven solution design using Generative and Agentic AI, prompt engineering, and evaluation frameworks. Serve as a subject matter expert, collaborate across teams, make technical decisions, mentor engineers, and align scalable AI and data capabilities with business objectives.