Top Data Engineer Jobs in Bangalore

Design, develop, and maintain scalable data solutions, pipelines, and ETL workflows using Databricks, Python, and PySpark. Ensure data quality, integrity, security, and performance across distributed systems. Lead data architecture initiatives, troubleshoot complex issues, coordinate with stakeholders, make team decisions, and mentor engineers while aligning data engineering efforts with organizational goals.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, support large-scale data processing, troubleshoot complex data issues, and optimize workflows. Lead team decisions, collaborate cross-functionally, mentor junior engineers, and serve as a subject matter expert in Databricks and cloud-based data engineering.

Designs, develops, and maintains scalable data solutions, pipelines, and ETL processes using Databricks, Python, and PySpark. Ensures data quality, supports large-scale distributed processing and migration, troubleshoots complex data issues, and promotes security and governance best practices. Leads architecture decisions, coordinates with stakeholders, manages team activities, mentors engineers, and serves as a subject matter expert across multiple teams.

Design, develop, and maintain data solutions, pipelines, and ETL processes using Databricks, Azure Databricks, PySpark, and Python. Ensure data quality, support large-scale data integration, troubleshoot complex issues, and optimize distributed data workflows. The role also involves making team decisions, collaborating cross-functionally, mentoring junior engineers, and serving as a subject matter expert.

Build and operate cloud-based data solutions, pipelines, and modern data architectures. Ingest, cleanse, normalize, and structure diverse and unstructured datasets using relational and non-relational databases, streaming technologies, data lakes, and event-driven processing. Partner with global cross-functional teams to support enterprise reporting, analytics, and application use cases across business functions.

Develop and support ETL and data transformation jobs for a global data warehouse using PySpark, Python, IBM DataStage, SQL, and AWS. Responsibilities include code reviews, integration testing, production support, root cause analysis, performance tuning, incident resolution, technical documentation, source-to-target mapping, and Control-M job monitoring. The role collaborates with global business and technical teams while managing multiple priorities.

Lead the design and modernization of ATOSS’s data platform into a scalable lakehouse architecture. Build reliable batch and streaming pipelines using Databricks, Spark, Delta Lake, and Kafka; establish standards for ingestion, transformation, governance, quality, lineage, performance, and observability; and enable AI and GenAI workloads including RAG, embeddings, and vector retrieval. Provide architectural direction, mentor engineers, and collaborate with AI, software, product, and architecture teams.

Designs and supports enterprise-scale data engineering solutions for asset management, including market, trade, position, security master, and investment data. Responsibilities include building Python and PL/SQL pipelines, Snowflake data environments, Kafka integrations, ETL/ELT processes, quality controls, scheduling, production support, root cause analysis, resilience, compliance, and platform modernization. The role partners with investment, operations, risk, compliance, and technology stakeholders.

Leads enterprise data engineering for asset management, designing pipelines and ETL/ELT solutions for market, trade, position, and reference data. Develops PL/SQL, Python, Shell, and Perl solutions; supports Snowflake, Kafka, scheduling platforms, production systems, data quality, reconciliation, monitoring, and disaster recovery. Partners with investment, risk, compliance, and technology teams while driving modernization, architecture, operational resilience, and regulatory support.

Designs, develops, and supports enterprise ETL/ELT pipelines, data warehouses, and integrations using SQL Server, SSIS, and Azure Data Factory. Responsibilities include modernizing legacy SSIS workloads, optimizing SQL performance, integrating APIs and SFTP, ensuring data quality, troubleshooting production issues, implementing CI/CD, and supporting hybrid cloud environments. The role also contributes to architecture decisions and may incorporate AI-enabled data engineering, anomaly detection, and intelligent automation.

Design, build, and optimize telematic data pipelines and architectures using SQL and AWS. Assemble large datasets, automate processes, support analytics and cross-functional teams, and improve data delivery, scalability, and operational insights.

Build scalable Snowflake data pipelines and ETL/ELT processes for structured and semi-structured data. Ensure data quality, security, and performance through advanced SQL, Snowpipe, Streams, Tasks, and stored procedures. Collaborate with architects, engineers, and business teams to translate requirements into solutions, support analytics and reporting, contribute to CoE guidelines and frameworks, and participate in proof-of-concept initiatives.

Designs, develops, and optimizes scalable AWS-based ETL/ELT pipelines using SQL and Python. Maintains data platforms with services such as Redshift, Lambda, S3, Glue, IAM, and CloudWatch; monitors reliability and performance; writes tests; performs code reviews; documents solutions; and collaborates with BI, AI/ML, and infrastructure teams.

Manage product master data across SAP S/4HANA, PLM, and legacy systems, including material masters, BOMs, and variant configurations. Validate data quality, resolve order fallouts and discrepancies, support governance and ISO 9000 documentation, and develop operational reporting. Identify process improvements, automation opportunities, and AI-enabled workflow enhancements while collaborating with cross-functional teams on testing, standardization, and digital transformation initiatives.

Build and optimize batch and streaming data pipelines, develop data models and transformations, integrate APIs and event streams, and implement data quality, testing, documentation, and monitoring. The role uses Snowflake, SQL, Python, dbt, orchestration tools, and cloud platforms while supporting DataOps, scalability, cost efficiency, and collaboration with engineers, analysts, data scientists, and business stakeholders.

Manage SAP master data creation and maintenance, support internal users, resolve data issues, improve processes, analyze data, automate tasks, and maintain Power BI reports. The role requires collaboration with cross-functional teams, knowledge sharing, and training. Candidates need a Mechanical Engineering degree, at least four years of SAP master data experience, advanced Excel skills, and understanding of manufacturing processes. The position is onsite in Bengaluru with minimal travel.

Design, develop, and maintain scalable AWS data pipelines and ETL processes. Manage and optimize Redshift, PostgreSQL, and Oracle databases; write efficient SQL and Python/PySpark scripts; and use Informatica PowerCenter. Ensure data quality, collaborate with cross-functional teams, and support data warehousing solutions. Preferred experience includes Jenkins, SVN, Postman, Agile methodologies, and data warehousing best practices.

Senior individual-contributor and hands-on technical leader building scalable data infrastructure and analytics for private markets. Architect and oversee data pipelines/platforms (Snowflake, SQL, Python), define engineering standards, champion data governance, drive AI/ML adoption, and manage data engineering delivery and a team while aligning technical solutions with business priorities.

Design and build scalable, multi-tenant data platforms, warehouses, and lakehouse architectures. Develop batch, streaming, and CDC ingestion pipelines using Python, SQL, Spark, AWS, and streaming technologies. Apply data modeling, Data Vault, governance, data quality, schema evolution, orchestration, and performance optimization practices to support analytics, operational insights, and AI-driven products.

Build and maintain scalable BigQuery data solutions, including tables, views, stored procedures, and data models. Manage SQL deployment manifests and multi-environment rollouts through GitLab CI/CD. Improve data quality, governance, performance, security, and accessibility across large-scale datasets. Collaborate with architects, data scientists, engineers, and global stakeholders while contributing to AI-enabled initiatives, code reviews, technical leadership, and team mentoring.

Designs and leads large-scale data engineering solutions and ETL migrations to Google Cloud Platform. Responsibilities include gathering requirements, designing and deploying scalable data pipelines, migrating legacy JCL and ETL jobs, integrating data sources, monitoring workflows, troubleshooting production issues, performance tuning, testing, estimating development efforts, and coordinating with stakeholders and engineering teams.

Lead the design, modernization, migration, and operation of large-scale data warehouses, data lakes, analytics platforms, and ETL pipelines on GCP. Gather requirements, develop migration strategies, build and optimize scalable data workflows, troubleshoot production issues, coordinate deployments, and mentor engineering teams. The role also involves stakeholder collaboration, effort estimation, technical leadership, performance tuning, job monitoring, and client-facing consulting.

Develop and support scalable data operations, ETL pipelines, SQL integrations, Azure services, Power Platform automations, and Power BI reporting. Responsibilities include Azure Data Factory pipeline development, Logic Apps and Power Automate workflows, Power BI modeling and publishing, Azure resource administration, governance, monitoring, troubleshooting, security controls, documentation, and performance optimization.

Build and optimize AWS data lake pipelines for Customer Master and Product Information Management data. Develop ingestion, cleansing, transformation, curation, provisioning, testing, governance, cataloging, and data quality solutions using Python, PySpark, and Spark SQL. Collaborate with data governance stakeholders on stewardship, matching, survivorship, and lifecycle management. Configure and analyze Reltio and Stibo platforms, perform data modeling and analysis, and support agile DevOps delivery.

Designs, develops, and maintains scalable ETL and data integration pipelines using Snowflake, AWS Glue, Fivetran, and Snowpipes. Responsibilities include data transformation, quality validation, performance tuning, monitoring, troubleshooting, documentation, and data lineage. The role collaborates with architects, modelers, and source-system owners to integrate databases, APIs, files, and other data sources. It requires advanced SQL, scripting, relational and NoSQL database knowledge, and substantial ETL engineering experience.