Top Data Engineer Jobs in Bangalore

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.

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.

Leads the architecture and implementation of scalable AWS data platforms, including ETL/ELT pipelines, event-driven systems, data models, query optimization, AI/ML infrastructure, governance, security, compliance, monitoring, and data quality standards.

Designs and optimizes enterprise data pipelines using Azure Data Factory, Azure Data Lake Storage, SQL, Python, and cloud data platforms. Owns pipeline reliability, troubleshooting, automation, CI/CD, data lineage, documentation, code standards, and technical audits. Supports analytics and media teams while leading modernization initiatives involving Databricks, dbt, and DevOps. Serves as a subject matter expert for pipeline architecture, orchestration, schema evolution, monitoring, governance, and high-availability data processing.

Modernize legacy Hadoop and Spark workflows by migrating them to AWS and Databricks. Build production data pipelines optimized for AI use cases, including unstructured data ingestion, tokenization, metadata tagging, and vector database integration. Develop AI agents using LLMs such as Anthropic Claude, maintain legacy workloads, optimize Delta Lake performance, and support BI stakeholders with dashboard troubleshooting and data extract improvements.

Extract and maintain support data, build pipelines and dashboards, analyze ticket trends and customer journeys, identify automation opportunities, and translate insights into prioritized improvements. The role implements Zendesk workflows and APIs, supports AI and automation initiatives, runs experiments, measures business impact, and communicates findings to technical and non-technical stakeholders.

Build and operate production batch and streaming data pipelines from ingestion through transformation, reconciliation, quality validation, and client delivery. Design CDC, incremental loads, medallion datasets, dimensional models, metadata-driven components, and secure multi-tenant platforms using Azure Databricks, Snowflake, SQL Server, and Databricks. Own GitHub workflows, automated testing, CI/CD, deployment, monitoring, incident response, and data lineage while partnering with product, valuation, client-facing, and technical teams.

Design and maintain scalable pipelines for visual and voice data, including ingestion, processing, validation, testing, observability, and automation. Build cloud, on-premise, and containerized data infrastructure; optimize storage, retrieval, and caching for large media assets; debug complex data issues; and collaborate with data scientists, operators, and product teams to support machine learning workflows.

Designs and leads scalable GCP data platforms, pipelines, models, and ETL workflows across ingestion through consumption. Owns BigQuery architecture, performance, cost optimization, data quality, governance, observability, testing, CI/CD, and monitoring. Establishes engineering standards, evaluates technologies, shapes the technical roadmap, and mentors engineers. Partners with technical and business leaders to solve complex data problems and translate ambiguous needs into reliable analytics solutions.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Modernize legacy Hadoop and Spark infrastructure by migrating workloads to AWS and Databricks. Build production data pipelines optimized for AI use cases, including unstructured data ingestion, tokenization, metadata tagging, and vector search. Develop AI agents using LLMs such as Anthropic Claude, maintain legacy workloads, optimize Delta Lake performance, and support BI users by troubleshooting dashboards, extracts, and report latency.

Design, implement, and maintain data quality solutions using Informatica (IDMC/IICS/IDQ) and PIM. Perform profiling, scorecarding, transformations, reporting, troubleshooting, and stakeholder training. Collaborate on data governance and produce DQ metrics and documentation.

Designs, builds, deploys, and supports secure, scalable cloud data solutions and pipelines for enterprise clients. Translates business requirements into data architectures, integration patterns, and models; ensures performance, reliability, governance, and documentation. Provides technical leadership during reviews and workshops, collaborates with engineering and analytics teams, contributes reusable accelerators and best practices, mentors colleagues, and manages client engagements through delivery.