Top Data Engineer Jobs in Bangalore

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.

Design, build, and maintain scalable data platforms and pipelines using AWS, Snowflake, Databricks, and dbt. Implement infrastructure as code (Terraform), CI/CD (GitHub/GitLab), data governance, and security. Partner with analytics and product teams to provide reliable, feature-rich datasets for AI/ML applications and ensure data lineage, compliance, and quality.

Design, build, and optimize scalable Azure cloud data platforms and high-performance ETL pipelines using Synapse, ADF, Spark, SQL, and Python. Implement data models, DataOps/CI-CD, quality checks, cost and performance optimizations, troubleshoot production issues, and collaborate with clients and stakeholders while mentoring junior engineers.

Design, build, and productionize end-to-end cloud data solutions and pipelines. Translate business needs into technical architectures, ensure performance, security, scalability, and reliability in production. Deliver client engagements on time, produce technical documentation and runbooks, present solutions to stakeholders, mentor peers, and contribute reusable assets and best practices across the firm.

Lead design, development, and delivery of real-time Apache Flink ETL pipelines to sync PostgreSQL to StarRocks. Build CDC solutions with Flink CDC/Debezium, optimize performance, ensure reliability, run Flink on GKE with Helm and Harness CI/CD, monitor with Prometheus/Grafana, mentor engineers, and maintain operational runbooks and data quality processes.

Design, develop, and optimize scalable cloud-native AWS data platforms and high-performance batch/analytical pipelines using Spark/PySpark and Apache Iceberg. Implement Lakehouse solutions, optimize query and storage performance, ensure observability, data quality, and production support, and follow CI/CD and data engineering best practices while collaborating with stakeholders.

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

Design, build, and optimize scalable batch and streaming data pipelines, manage Snowflake and BigQuery warehouses, create dbt data models, ensure data governance and observability, collaborate with architecture and security teams, write efficient SQL/Python/PowerShell, and participate in a light on-call rotation to deliver reliable data products.

Lead design and implementation of cloud-native ETL/ELT pipelines and data warehouse solutions. Optimize performance, ensure data quality and governance, mentor engineers, automate workflows, and collaborate with cross-functional teams to support analytics and BI initiatives.

Design, build, and maintain scalable batch and streaming data pipelines and data models using cloud-native technologies. Improve platform performance, reliability, observability, and automation; perform data quality validation, troubleshoot production issues, participate in architecture and code reviews, and mentor junior engineers.

Lead Azure Data Engineer to design, build, and operate petabyte-scale data systems on Azure. Lead a data engineering team to develop ETL pipelines (ADF, Synapse, Databricks), perform data modelling, migrations, monitoring, security, and collaborate with BI/ML/Product teams while mentoring junior engineers and improving analytics and dashboarding.

Design and implement data quality processes, profile and cleanse data, deploy cloud-based data quality solutions (Azure/AWS/GCP), document issues and remediation, report metrics, and collaborate with data teams and stakeholders.

Design, build, and maintain scalable cloud-native batch and streaming data platforms and pipelines. Own end-to-end solutioning, deployment, production support, performance tuning, monitoring, and mentorship while collaborating with product, analytics, and infrastructure teams.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Senior technical leader responsible for designing, building, and scaling cloud-native data platforms and pipelines. Partners with stakeholders, mentors engineers, enforces engineering best practices, ensures data modeling, governance, and quality, debugs production issues, and contributes to architecture, design, and Agile delivery to support business outcomes.

Design, build, and operationalize analytics-ready data products, semantic models, and self-service BI. Develop transformation pipelines (dbt, SQL, PySpark) on Databricks, ingest ERP and SaaS sources, implement data quality and governance, and partner with business stakeholders to deliver dashboards and reusable datasets.

Design, build, and optimize Azure-based ETL/ELT data pipelines using PySpark and Spark SQL on Databricks. Implement hybrid integrations, enforce data governance and quality, support CI/CD and version control, collaborate with analysts and data scientists, and troubleshoot pipeline reliability and performance.

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.

Design, build and maintain Oracle Fusion Data Intelligence analytics for Finance and SCM. Extend prebuilt content, model and augment data, configure pipelines, implement role- and row-level security, optimize performance, troubleshoot loads, produce documentation, and mentor stakeholders while owning the FDI environment.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Lead design and implement ETL pipelines using Informatica MDM, develop and optimize complex SQL/PLSQL/T-SQL stored procedures, model enterprise data warehouses, automate ingestion with Python, troubleshoot ETL performance, enforce data governance, and collaborate with cross-functional teams to ensure data quality and scalable modern data platform architectures.

Design and implement scalable ETL and real-time/batch pipelines using Azure Data Factory, Synapse, and Spark; build and optimize lakehouse and warehouse analytics, integrate Kusto/Delta Lake sources, tune SQL, develop Power BI reports with DAX, maintain secure Azure data architectures, and document technical processes.

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

Design, build, and operate Webex's big data platform and streaming pipelines. Develop and tune Kafka-based ingestion/streaming workloads and Spark/Flink processing jobs. Manage analytical datasets with Iceberg and query engines like Trino/Presto. Improve performance, scalability, and cost-efficiency, write testable code, and participate in code reviews.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.