Top Data Engineer Jobs in Bangalore

Designs, develops, and maintains scalable batch and streaming data pipelines using Python, PySpark, Spark, Databricks, SQL, and AWS. Leads data modeling and solution design, builds high-volume ingestion and transformation workflows, optimizes distributed workloads, and applies data warehousing best practices. Performs debugging, query tuning, code reviews, version control, and CI-friendly development while partnering with stakeholders and validating AI-assisted engineering outputs.

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.

Lead the technical evolution of Atlassian’s data engineering capabilities by architecting scalable batch and streaming data solutions. Direct complex engineering efforts, improve data quality and productivity, establish technical direction, and coordinate implementation across teams. Mentor and coach data engineers while partnering cross-functionally to support data-driven business decisions and long-term platform scalability.

Designs, develops, and maintains scalable batch and real-time data pipelines, ETL/ELT workflows, data models, and cloud data platforms. Ensures data quality, governance, security, observability, and performance while supporting cloud modernization. Collaborates with product owners, architects, business stakeholders, and data scientists, and maintains technical documentation, lineage, monitoring, and operational runbooks.

Designs and maintains scalable data pipelines and cloud-native solutions for analytics, data science, and AI applications. Responsibilities include automated data ingestion and processing, data quality and performance optimization, cross-functional problem solving, and evaluating new technologies. The role requires cloud, big data, programming, distributed systems, and containerization expertise, and is primarily onsite at an HPE office.

Design, develop, test, implement, and support cloud-based data engineering solutions. Lead developers working with machine learning, distributed microservices, and full-stack systems. Use Java, Scala, Python, databases, data warehouses, streaming technologies, and public clouds. Collaborate with product managers and Agile teams, conduct code reviews and unit testing, optimize performance, mentor engineers, and stay current with emerging technologies.

Designs and maintains batch and real-time ETL/ELT pipelines, data lakes, warehouses, lakehouses, schemas, and curated datasets. Develops responsive web applications and scalable backend services, optimizes performance, applies security and privacy standards, and collaborates with technical and business stakeholders in an Agile environment. Troubleshoots incidents, performs root-cause analysis, documents solutions, and improves platform reliability.

Design, build, and operate end-to-end cloud data pipeline solutions with a focus on reliability, scalability, security, and zero-downtime releases. Monitor and optimize performance, resolve incidents, support production lifecycle, and collaborate with product teams to deliver data products for finance, payments, and regulatory reporting.

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.

Build and optimize scalable data engineering pipelines, feature stores, data models, and workflows for structured, semi-structured, and unstructured data. Integrate diverse data sources using AWS and Snowflake, implement transformations and quality checks, and support data governance, security, and management. The role also involves web crawling, API and stream-based extraction, workflow automation, root-cause analysis, and collaboration with cross-functional stakeholders in a hybrid HPE office environment.

Build and maintain data systems and pipelines using GCP, BigQuery, GCS, and Dataflow. Analyze and organize raw data, combine information from multiple sources, improve data quality, develop algorithms and analytical tools, and prepare datasets for predictive and prescriptive modeling. The role collaborates with data scientists and architects while evaluating business needs, interpreting trends, and reporting analytical results.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

Build scalable Snowflake data pipelines and ETL/ELT processes across projects. Ensure data quality, security, performance, and support structured and semi-structured data for analytics. Collaborate with architects, engineers, business teams, and Team Leads to translate requirements into solutions. Contribute to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and GenAI initiatives using Snowflake Cortex capabilities.

Designs, develops, and maintains data solutions, pipelines, ETL processes, and data models. Ensures data quality, supports data migration across systems, and collaborates with data scientists, analysts, and cross-functional teams to meet analytical and business requirements. The role requires Databricks expertise and familiarity with programming, data integration, cloud data services, and data warehousing.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality, governance, performance, and reliability across data systems. Monitor and troubleshoot pipeline issues, document workflows, collaborate with cross-functional teams, and provide technical guidance to junior team members. The role requires expertise in Databricks and at least three years of relevant experience.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, support data migration across systems, monitor and optimize pipeline performance, troubleshoot data issues, document workflows, and collaborate with cross-functional teams. The role also requires AI agent and workflow integration expertise, independent execution, subject matter expertise, and mentoring junior team members.

Design and implement scalable, secure Snowflake architectures and data pipelines. Lead data ingestion, ETL, transformations, performance tuning, security controls, and best practices. Mentor teams, support presales, run POCs, and standardize data modeling across projects using Snowflake features and integration tools like dbt and Fivetran.

Designs, develops, and maintains scalable Azure-based data pipelines, ETL frameworks, and data architectures using Databricks, Spark, and cloud storage. Ensures data quality, performance, reliability, and documentation; monitors and troubleshoots workflows; manages CI/CD deployments; and collaborates with cross-functional teams. Acts as a subject matter expert, leads technical decisions, oversees continuous improvement, and mentors junior engineers.

Design, develop, and maintain scalable Azure-based data pipelines, ETL processes, and data architectures using Databricks and related Azure services. Ensure data quality, integrity, performance, and reliability while monitoring and troubleshooting workflows. Manage CI/CD deployments, document processes, collaborate with cross-functional teams, lead technical decisions, oversee workflow improvements, and mentor junior engineers.

Design, develop, and maintain scalable Azure-based data pipelines, ETL processes, and data architectures using Databricks and related services. Ensure data quality, optimize performance, monitor workflows, manage CI/CD deployments, and troubleshoot issues. Collaborate with data and business teams, lead technical decisions, mentor junior engineers, and oversee continuous improvement of reliable, reusable data solutions.

Designs, develops, and maintains scalable Azure-based data pipelines, ETL processes, and data architectures. Ensures data quality, integrity, performance, and reliability across storage and processing systems. Uses Databricks, Azure services, Spark, and programming languages to build automated data solutions. Responsibilities include monitoring workflows, troubleshooting issues, managing CI/CD deployments, documenting processes, collaborating across teams, making technical decisions, leading implementation efforts, and mentoring junior engineers.

Design, develop, and maintain data solutions, pipelines, and ETL processes for data generation, integration, migration, and deployment. Ensure data quality, optimize workflows and system performance, and support large-scale data processing and warehousing. Act as a subject matter expert, lead team decisions, collaborate across teams, mentor junior engineers, and provide solutions to complex data engineering challenges.

Designs, develops, and maintains data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration across systems, optimizes workflows for performance and reliability, documents processes, collaborates with stakeholders, and mentors junior team members.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, governance, and workflow optimization while supporting data migration and deployment across systems. Lead architecture decisions, troubleshoot data issues, collaborate across teams, and mentor junior engineers.

Designs, develops, and maintains data solutions, pipelines, and ETL processes using Databricks, Azure Databricks, Python, and PySpark. Ensures data quality, supports large-scale processing and integration, troubleshoots complex data issues, and optimizes pipeline performance and scalability. The role serves as a subject matter expert, mentors junior engineers, coordinates cross-functional initiatives, and contributes to team and organizational data engineering decisions.