Top Data Engineer Jobs in Bengaluru

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design and optimize enterprise data models and pipelines integrating SAP Core, PostgreSQL, MySQL and legacy systems. Implement data cataloging, lineage and metadata management. Tune SQL and database performance, build ETL/ELT pipelines, and collaborate with architects and global teams to deliver scalable data architecture for a new strategic initiative.

Design, build, and maintain security observability dashboards (vulnerability scans, remediation, traceability). Gather requirements, develop interactive dashboards using Power BI and web tech, collaborate cross-functionally, and provide training and ongoing maintenance.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

The Senior Data Engineer will build and optimize data pipelines in Azure Databricks, migrate existing pipelines from Azure Synapse, and design scalable data models to support a financial services client's data platform modernization.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Design, operate, and secure scalable batch and streaming data pipelines and platforms. Own ingestion, transformation, serving, data quality, and access controls. Partner with product, risk, compliance, and InfoSec to ensure production reliability, correctness, and audit readiness for financial data.

Design, build, and optimize scalable data pipelines and models using Spark, Kafka, Airflow, dbt, and SQL across GCP and AWS. Ingest and normalize marketing and analytics data, ensure data quality and governance, support analytics and reporting, and collaborate with cross-functional global teams.

Lead design and deliver scalable data platforms and pipelines (batch & streaming) on Databricks Lakehouse. Implement medallion architecture, ETL/ELT, governance, security, observability, and cost/performance tuning. Build retrieval-ready datasets, embeddings, and vector stores for LLM/GenAI use cases. Lead migrations, enforce engineering best practices, mentor engineers, and collaborate with architects, data scientists, and stakeholders to enable analytics and GenAI applications.

Design, build, and operate high-throughput, low-latency Kafka-based streaming architectures and data pipelines using Flink, Java, and PySpark. Manage and tune Kafka clusters, implement HA and disaster recovery, apply security (SSL/mTLS, SASL, ACLs), automate deployments with IaC (Terraform) and GitOps, and collaborate with cross-functional teams to deliver enterprise-grade real-time data solutions.

Design and implement Lakehouse architecture with OneLake and Fabric, build medallion Delta table layers, migrate on‑prem SQL Server/SSIS to Fabric pipelines and notebooks, reverse‑engineer and document complex SQL logic, produce data mappings and dependency docs, and collaborate with functional teams to support migration and simplification of legacy logic.

Design, develop, and optimize distributed PySpark data pipelines using the Apache ecosystem (Hadoop, Hive, HDFS). Build scalable ETL workflows, implement data quality checks and lineage, integrate structured and unstructured sources, collaborate with data scientists and stakeholders, tune performance, and maintain code in Python/Java with version control and CI/CD.

Design, build, and maintain scalable AWS-based big data pipelines and ETL workflows using Spark/Hadoop. Collaborate with engineers and analysts to optimize data storage, ensure data quality/security, troubleshoot ingestion and transformation issues, and implement data lake and warehousing solutions.

As a Data Engineer, you'll build and monitor large-scale data pipelines, develop the DBT setup for data transformation and solve customer problems using your SQL skills.

Design, build, and optimize large-scale ETL pipelines and distributed data applications (batch and streaming) using Spark and Scala. Ensure data quality and performance, deploy and manage workloads in cloud/container environments, troubleshoot production issues, and collaborate with product, engineering, and analytics teams to deliver scalable AdTech data solutions.

Lead and mentor a data engineering team to design, build, and optimize scalable batch and real-time data pipelines using Databricks, Spark, Delta Lake and cloud services (Azure/AWS). Implement Medallion architecture, ETL/ELT workflows (ADF/Databricks Workflows), CI/CD and IaC, ensure data governance, performance, and stakeholder collaboration.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.