Top Data Engineer Jobs in Bangalore

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.

Analyze business and user requirements across financial services value streams, define technical requirements, develop cloud solution options, and manage business and IT stakeholders. The role requires understanding existing framework capabilities, complex data models, cross-functional solution design, clear documentation, team coordination across regions, and familiarity with the SDLC from design through testing and implementation.

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.

Own and improve scalable batch and incremental data pipelines, data models, and analytical products supporting reliability reporting. Build automated data quality checks, anomaly detection, monitoring, and alerts; optimize SQL, Spark, dbt, and Databricks performance and costs; enable Tableau dashboards; maintain engineering standards, CI/CD, documentation, and runbooks; and mentor data engineering peers.

Design, build, and maintain scalable data pipelines and API-based ETL/ELT processes. Develop data products, semantic layers, data quality and observability frameworks, lineage tracking, monitoring, and alerting. Collaborate with business teams to gather requirements, support CI/CD processes, validate data, and scale the data architecture.

Lead data engineering architecture and execution for large-scale, low-latency analytics. Own technical direction, ensure operational data quality, design streaming and batch pipelines, mentor engineers, coordinate cross-functional teams, and deliver scalable solutions using Spark, Airflow, and AWS or equivalent data platforms.

Design, develop, and maintain data solutions and ETL pipelines using AWS services, PySpark, SQL, and Glue. Migrate and transform high-volume data from on-premises systems to AWS, validate data quality, troubleshoot workflows, optimize performance and costs, and support incremental loads. Collaborate with solution designers, DevOps engineers, and cross-functional teams to test, deploy, document, and improve scalable data pipelines.

Design, develop, and maintain data solutions, pipelines, and ETL processes using AWS Big Data services. Ensure data quality, migrate data across systems, and work with Python, PySpark, SQL, Spark, and Hadoop. Collaborate with stakeholders, contribute to code reviews and technical discussions, solve development problems, and independently serve as a subject matter expert.

The job description does not include specific responsibilities or qualifications because the position details were replaced with a placeholder. Based on the title, this is a principal-level role focused on data platform engineering.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality, optimize distributed data workflows, troubleshoot performance issues, and deploy data across systems. Collaborate with cross-functional teams to understand requirements, document processes, and provide technical guidance to junior team members. The role requires independent execution, subject matter expertise development, and proficiency in PySpark, data modeling, storage solutions, and cloud-based data platforms.

Build and maintain batch and streaming data pipelines, dimensional models, ETL/ELT workflows, data quality monitoring, and warehouse schemas. The role involves optimizing queries and pipelines, applying HIPAA and PII controls, supporting stakeholders and data scientists, documenting technical designs, and participating in data-platform on-call operations. Required expertise includes Python, SQL, distributed systems, databases, Spark, Kafka, Airflow, and data warehousing.

Design, build, and maintain end-to-end ETL/data pipelines to migrate and transform data from on-prem legacy systems to AWS. Ensure data quality, incremental loads, monitoring, troubleshooting, and collaborate across teams for performance testing and deployment.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using AWS Glue and PySpark. Ensure data quality, support data migration across systems, troubleshoot processing issues, and optimize cloud-based workflows. The role also involves leading team decisions, coordinating cross-functional initiatives, mentoring junior engineers, and serving as a subject matter expert.

Design, develop, and maintain end-to-end ETL/data pipelines to migrate on-prem legacy data to AWS. Use AWS Glue (PySpark/SQL), Step Functions, EventBridge, and Lambda; ensure data quality, incremental loads, monitoring with CloudWatch, performance tuning, error handling, and documentation. Collaborate with solution designers and DevOps for testing and deployment.

Designs and maintains Azure-based ETL/ELT pipelines using Azure Data Factory, Snowflake, and DBT. Develops SQL transformations, integrates diverse data sources, monitors pipeline performance, and ensures data quality, governance, and documentation. Collaborates with stakeholders, analysts, architects, and DevOps teams to deliver scalable cloud-native data solutions.

Design, build, and maintain scalable ETL/ELT pipelines in GCP (BigQuery, Dataform, Airflow) to integrate data from systems (Salesforce, NetSuite, APIs), build AI agents to automate finance workflows, support production month-end jobs in PST, manage IaC with Terraform, maintain CI/CD in GitLab, and produce documentation and BI/ML support for finance stakeholders.

Build, maintain, test, and optimize scalable data architectures and platforms. Extract, transform, and load data for analysts and scientists; source and integrate new data; apply data quality testing, cleansing, monitoring, and modelling. Collaborate with technology and architecture teams, adopt DevOps practices, resolve root causes, manage platform costs, and develop knowledge of data structures and metrics.

Design, build, and maintain scalable data pipelines and dbt models across Bronze/Silver/Gold layers in a Medallion architecture. Develop custom dbt macros, write production Python for ingestion and transformations, orchestrate pipelines with Dagster/Airflow, implement GitLab CI/CD, and ensure data quality using dbt tests or Great Expectations.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality through validation, cleansing, and monitoring, while supporting data migration and deployment across systems. Lead data architecture decisions, collaborate with stakeholders and multiple teams, troubleshoot complex data issues, and mentor team members. The role requires expertise in data modeling, database systems, and data warehousing, along with team leadership and technical decision-making.

Design, develop, and maintain scalable data solutions and ETL pipelines using Databricks. Ensure data quality, optimize workflows, mentor team members, and lead technical decisions across teams.

Design, develop, and maintain data solutions and ETL pipelines using Databricks; ensure data quality, scalability, and performance; troubleshoot data integration issues; lead and mentor team members and coordinate cross-functional data initiatives.

Design, develop, and maintain data solutions, scalable pipelines, and ETL processes using Microsoft Azure Databricks. Ensure data quality, governance, security, and efficient data integration across systems. Lead workflow optimization, troubleshoot complex issues, mentor junior engineers, manage team decisions, and coordinate cross-functional data initiatives.

The Data Engineer will optimize the elluminate® platform for clinical data through coding, ETL processes, and deliver client-focused solutions while ensuring compliance with industry standards.

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.

Build and maintain production ETL/ELT pipelines, CI/CD workflows, data ingestion capabilities, validation, monitoring, and reusable automation components. Apply Python, SQL, data modeling, warehousing, distributed processing, and cloud platforms to deliver scalable enterprise data solutions. Collaborate with product managers, analysts, engineers, and stakeholders in agile teams while translating business requirements into technical designs and optimizing data performance and cost.