Top Data Engineer Jobs in Bangalore

Build and maintain high-performance data infrastructure and real-time pipelines for cloud-based autonomous systems. Design, implement, document, unit-test, deploy, and integrate data engineering components. Develop CI/CD workflows, troubleshoot systems, monitor AWS environments, estimate development work, and collaborate with stakeholders while ensuring secure, optimized, high-quality deliverables.

Leads complex data engineering initiatives, including scalable data architecture, data platform development, data modeling, proof-of-concept projects, and performance optimization. Collaborates with engineering, product, platform, security, and architecture teams to deliver reliable solutions. Mentors data engineers, drives engineering standards, advises on technical tradeoffs, and promotes consistent data practices across the organization.

Manage, validate, migrate, and QA/QC environmental data in EQuIS and related systems; configure workflows, build Power BI dashboards, support reporting, automate processes, and prepare documentation and SOPs to ensure data quality and compliance across global projects.

Designs and maintains automated data quality checks for game telemetry, player events, and backend systems. Monitors pipelines, detects anomalies and schema changes, defines data quality standards and SLAs, investigates root causes, and builds dashboards and alerting. Collaborates with engineering, analytics, and product teams on event instrumentation, data lineage, privacy compliance, and reliable datasets for strategic decisions.

Supports Aftersales data extraction, cleaning, validation, and basic ETL pipeline maintenance. Updates Excel performance dashboards, prepares PowerPoint presentations, and collaborates with service engineers to interpret data. Requires a recent computer science, IT, or data engineering graduate with foundational knowledge of databases, SQL, ETL processes, Excel, and PowerPoint.

Design, build, and maintain scalable batch and real-time data pipelines and cloud-native data platforms (Lakehouse/Data Warehouse). Ensure data quality, security, automation, and performance tuning; collaborate with analytics and product teams; mentor junior engineers and produce design and test documentation.

Designs, develops, optimizes, and supports scalable Snowflake and AWS data platforms. Responsibilities include building ETL/ELT pipelines, developing data models, improving performance and cost efficiency, implementing CI/CD and DevOps automation, managing IaC with Terraform, and ensuring security, governance, monitoring, and operational excellence.

Lead development of the SIXT Data Platform using AWS and big data technologies. Architect hybrid data lake and warehouse solutions, automate ELT pipelines, and collaborate with engineers, analysts, and data scientists. Design multi-agent workflows for ingestion, transformation, validation, and distribution. Build LLM-powered capabilities including RAG pipelines, semantic data catalog search, and AI-assisted self-service. Apply strong engineering practices across Python, SQL, dbt, Airflow, CI/CD, testing, and distributed data systems.

The Senior Technical Consultant will manage complex data projects, build data pipelines, integrate systems, support deployments, and mentor junior staff.

Researches, acquires, normalizes, classifies, and maintains electronic component technical data using PIM and data quality tools. Develops product-category hierarchies, evaluates taxonomy and attribute relevance, identifies misclassified products, and recommends improvements to product data presentation and sourcing. Requires electronic component expertise, technical document proficiency, Excel skills, knowledge of component change notifications and life cycles, and a bachelor’s degree in electrical engineering or a related discipline.

Designs, builds, and supports modern Microsoft Fabric and Azure data solutions, including lakehouses, warehouses, ETL/ELT pipelines, dimensional models, and Power BI semantic models. Owns engineering workstreams from design through deployment and production support, applying security, governance, testing, observability, and CI/CD practices. Collaborates with architects and delivery teams, manages risks and dependencies, documents solutions, communicates with stakeholders, and mentors junior engineers through technical reviews and reusable engineering standards.

Own end-to-end architecture and technical delivery for complex Microsoft Fabric and Azure data-platform consulting engagements. Lead discovery workshops, define technical direction, assure security, governance, DataOps, performance, resilience, and production readiness, and resolve complex delivery issues. Oversee data modeling, integration, quality, lineage, and access controls across workstreams. Provide technical estimates and proposal input, act as an escalation point, and coach senior consultants and engineers.

Develop and support user-facing data applications and solutions on Palantir Foundry for Compliance Operations. Build API-driven ingestion pipelines, Python and SQL data pipelines, scalable data models, reusable datasets, and ontology assets. Translate stakeholder requirements into applications, troubleshoot across the data lifecycle, and support testing, deployment, production operations, performance improvements, and AI-enabled workflows.

Design and architect large-scale Azure data lakehouse infrastructure and ETL/ELT pipelines. Optimize Databricks and Data Lake costs, troubleshoot production failures, improve data quality and reliability, and implement governance and security best practices. Collaborate with and mentor global data scientists, analysts, and operations teams while delivering secure, automated solutions using Azure, Spark, Python, and Scala.

Build and maintain scalable data products using Snowflake, dbt, and Airflow. Responsibilities include dimensional data modeling, SQL transformations, pipeline orchestration, data quality testing, exploratory analysis, SLA monitoring, documentation, and stakeholder requirement gathering. Partner with analytics, product, engineering, and business teams to deliver reliable, production-ready datasets while following software engineering, CI/CD, testing, and deployment practices.

Lead data engineering for enterprise banking platforms, building ETL/ELT pipelines, Oracle and SQL/PLSQL solutions, data warehouses, and risk and regulatory reporting systems. Provide production support, troubleshoot batch and data issues, automate validation and reconciliation, ensure data quality and governance, and collaborate with Risk, Finance, Compliance, and Technology stakeholders. Mentor team members and support regulatory submissions and audit readiness.

The Sr Data Engineer I will provide consulting services for clinical systems, manage projects, and design SQL code for data reporting and analytics while ensuring compliance with industry standards.

Design, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.

Design, build, optimize, and maintain scalable ETL/ELT data pipelines and data platform components in an AWS/Snowflake ecosystem. Ensure data quality, governance, and near-real-time availability. Collaborate with and mentor junior engineers, contribute to architecture and data modeling, and implement performance tuning and best practices.

The Senior Software Developer will develop and support Qlik Sense applications, ensure best practices, and improve analytics processes while collaborating with clients and teams.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

Designs, develops, and supports large-scale Azure data engineering solutions using Azure Data Factory, Databricks, Synapse, data lakes, SQL, Python, and PySpark. The role requires cloud development, analytics, data platform administration, DevOps, CI/CD, infrastructure automation, and experience supporting multiple databases and Azure services in agile product environments.

Design, build, and operate production-grade batch ELT pipelines and a lakehouse (medallion pattern) in Databricks/Spark; ensure data quality, monitoring, SLAs, and create reusable data products while partnering with engineering and business stakeholders.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.