2652 Data Engineer jobs in Bengaluru

Role Overview

We are seeking an experienced Lead Data Engineer to join our Data Engineering team at Paytm, India's leading digital payments and financial services platform. This is a critical role responsible for designing, building, and maintaining large-scale, real-time data streams that process billions of transactions and user interactions daily. Data accuracy and stream reliability are essential to our operations, as data quality issues can result in financial losses and impact customer

a Lead Data Engineer at Paytm, you will be responsible for building robust data systems that support India's largest digital payments ecosystem. You'll architect and implement reliable, real-time data streaming solutions where precision and data correctness are fundamental requirements. Your work will directly support millions of users across merchant payments, peer-to-peer transfers, bill payments, and financial services, where data accuracy is crucial for maintaining customer confidence and operational excellence.

This role requires expertise in designing fault-tolerant, scalable data architectures that maintain high uptime standards while processing peak transaction loads during festivals and high-traffic events. We place the highest priority on data quality and system reliability, as our customers depend on accurate, timely information for their financial decisions. You'll collaborate with cross-functional teams including data scientists, product managers, and risk engineers to deliver data solutions that enable real-time fraud detection, personalized recommendations, credit scoring, and regulatory compliance reporting.

Key technical challenges include maintaining data consistency across distributed systems with demanding performance requirements, implementing comprehensive data quality frameworks with real-time validation, optimizing query performance on large datasets, and ensuring complete data lineage and governance across multiple business domains. At Paytm, reliable data streams are fundamental to our operations and our commitment to protecting customers' financial security and maintaining India's digital payments

Responsibilities

Data Stream Architecture & DevelopmentDesign and implement reliable, scalable data streams handling high-volume transaction data with strong data integrity controlsBuild real-time processing systems using modern data engineering frameworks (Java/Python stack) with excellent performance characteristicsDevelop robust data ingestion systems from multiple sources with built-in redundancy and monitoring capabilitiesImplement comprehensive data quality frameworks, ensuring the 4 C's: Completeness, Consistency, Conformity, and Correctness - ensuring data reliability that supports sound business decisionsDesign automated data validation, profiling, and quality monitoring systems with proactive alerting capabilitiesInfrastructure & Platform ManagementManage and optimize distributed data processing platforms with high availability requirements to ensure consistent service deliveryDesign data lake and data warehouse architectures with appropriate partitioning and indexing strategies for optimal query performanceImplement CI/CD processes for data engineering workflows with comprehensive testing and reliable deployment proceduresEnsure high availability and disaster recovery for critical data systems to maintain business continuity

Performance & OptimizationMonitor and optimize streaming performance with focus on latency reduction and operational efficiencyImplement efficient data storage strategies including compression, partitioning, and lifecycle management with cost considerationsTroubleshoot and resolve complex data streaming issues in production environments with effective response protocolsConduct proactive capacity planning and performance tuning to support business growth and data volume increases

Collaboration & Leadership Work closely with data scientists, analysts, and product teams to understand important data requirements and service level expectationsMentor junior data engineers with emphasis on data quality best practices and customer-focused approachParticipate in architectural reviews and help establish data engineering standards that prioritize reliability and accuracyDocument technical designs, processes, and operational procedures with focus on maintainability and knowledge sharing

Required Qualifications

Experience & EducationBachelor's or Master's degree in Computer Science, Engineering, or related technical field

7+ years (Senior) of hands-on data engineering experience

Proven experience with large-scale data processing systems (preferably in fintech/payments domain)

Experience building and maintaining production data streams processing TB/PB scale data with strong performance and reliability standards

Technical Skills & RequirementsProgramming Languages:

Expert-level proficiency in both Python and Java; experience with Scala preferred

Big Data Technologies: Apache Spark (PySpark, Spark SQL, Spark with Java), Apache Kafka, Apache Airflow

Cloud Platforms: AWS (EMR, Glue, Redshift, S3, Lambda) or equivalent Azure/GCP services

Databases: Strong SQL skills, experience with both relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra, Redis)

Data Quality Management: Deep understanding of the 4 C's framework - Completeness, Consistency, Conformity, and Correctness

Data Governance: Experience with data lineage tracking, metadata management, and data cataloging

Data Formats & Protocols: Parquet, Avro, JSON, REST APIs, GraphQLContainerization & DevOps: Docker, Kubernetes, Git, GitLab/GitHub with CI/CD pipeline experience

Monitoring & Observability: Experience with Prometheus, Grafana, or similar monitoring tools

Data Modeling: Dimensional modeling, data vault, or similar methodologies

Streaming Technologies: Apache Flink, Kinesis, or Pulsar experience is a plus

Infrastructure as Code: Terraform, CloudFormation (preferred)

Java-specific: Spring Boot, Maven/Gradle, JUnit for building robust data services

Preferred Qualifications

Domain Expertise

Previous experience in fintech, payments, or banking industry with solid understanding of regulatory compliance and financial data requirementsUnderstanding of financial data standards, PCI DSS compliance, and data privacy regulations where compliance is essential for business operationsExperience with real-time fraud detection or risk management systems where data accuracy is crucial for customer protection

Advanced Technical Skills (Preferred)

Experience building automated data quality frameworks covering all 4 C's dimensionsKnowledge of machine learning stream orchestration (MLflow, Kubeflow)Familiarity with data mesh or federated data architecture patternsExperience with change data capture (CDC) tools and techniques

Leadership & Soft SkillsStrong problem-solving abilities with experience debugging complex distributed systems in production environmentsExcellent communication skills with ability to explain technical concepts to diverse stakeholders while highlighting business valueExperience mentoring team members and leading technical initiatives with focus on building a quality-oriented cultureProven track record of delivering projects successfully in dynamic, fast-paced financial technology environments

The Role

We're looking for a senior AI engineer who can build production-grade agentic AI systems. You'll be working at the intersection of cutting-edge AI research and scalable engineering, creating autonomous agents that can reason, plan, and execute complex tasks reliably at scale.

What We Need

Agentic AI & LLM Engineering

You should have hands-on experience with:

Multi-agent systems: Building agents that coordinate, communicate, and work together on complex workflows

Agent orchestration: Designing systems where AI agents can plan multi-step tasks, use tools, and make autonomous decisions

LLMOps Experience: End-to-End LLM Lifecycle Management - hands-on experience managing the complete LLM workflow from prompt engineering and dataset curation through model fine-tuning, evaluation, and deployment. This includes versioning prompts, managing training datasets, orchestrating distributed training jobs, and implementing automated model validation pipelines. Production LLM Infrastructure - experience building and maintaining production LLM serving infrastructure including model registries, A/B testing frameworks for comparing model versions, automated rollback mechanisms, and monitoring systems that track model performance, latency, and cost metrics in real-time.

AI Observability: Experience implementing comprehensive monitoring and tracing for AI systems, including prompt tracking, model output analysis, cost monitoring, and agent decision-making visibility across complex workflows.

Evaluation frameworks: Creating comprehensive testing for agent performance, safety, and goal achievement

LLM inference optimization: Scaling model serving with techniques like batching, caching, and efficient frameworks (vLLM, TensorRT-LLM)

Systems Engineering

Strong backend development skills including:

Python expertise: FastAPI, Django, or Flask for building robust APIs that handle agent workflows

Distributed systems: Microservices, event-driven architectures, and message queues (Kafka, RabbitMQ) for agent coordination

Database strategy: Vector databases, traditional SQL/NoSQL, and caching layers optimized for agent state management

Web-scale design: Systems handling millions of requests with proper load balancing and fault tolerance

DevOps (Non-negotiable)

Kubernetes: Working knowledge required - deployments, services, cluster management

Containerization: Docker with production optimization and security best practices

CI/CD: Automated testing and deployment pipelines

Infrastructure as Code: Terraform, Helm charts

Monitoring: Prometheus, Grafana for tracking complex agent behaviors

Programing Language : Java , Python

What You'll Build

You'll architect the infrastructure that powers our autonomous AI systems:

Agent Orchestration Platform: Multi-agent coordination systems that handle complex, long-running workflows with proper state management and failure recovery.

Evaluation Infrastructure: Comprehensive frameworks that assess agent performance across goal achievement, efficiency, safety, and decision-making quality.

Production AI Services: High-throughput systems serving millions of users with intelligent resource management and robust fallback mechanisms.

Training Systems: Scalable pipelines for SFT and DPO that continuously improve agent capabilities based on real-world performance and human feedback.

Who You Are

You've spent serious time in production environments building AI systems that actually work. You understand the unique challenges of agentic AI - managing state across long conversations, handling partial failures in multi-step processes, and ensuring agents stay aligned with their intended goals.

You've dealt with the reality that the hardest problems aren't always algorithmic. Sometimes it's about making an agent retry gracefully when an API call fails, or designing an observability layer that catches when an agent starts behaving unexpectedly, or building systems that can scale from handling dozens of agent interactions to millions.

You're excited about the potential of AI agents but pragmatic about the engineering work required to make them reliable in production.

Role Overview

We are seeking an experienced Lead Data Engineer to join our Data Engineering team at Paytm, India's leading digital payments and financial services platform. This is a critical role responsible for designing, building, and maintaining large-scale, real-time data streams that process billions of transactions and user interactions daily. Data accuracy and stream reliability are essential to our operations, as data quality issues can result in financial losses and impact customer trust.

As a Lead Data Engineer at Paytm, you will be responsible for building robust data systems that support India's largest digital payments ecosystem. You'll architect and implement reliable, real-time data streaming solutions where precision and data correctness are fundamental requirements . Your work will directly support millions of users across merchant payments, peer-to-peer transfers, bill payments, and financial services, where data accuracy is crucial for maintaining customer confidence and operational excellence.

This role requires expertise in designing fault-tolerant, scalable data architectures that maintain high uptime standards while processing peak transaction loads during festivals and high-traffic events. We place the highest priority on data quality and system reliability, as our customers depend on accurate, timely information for their financial decisions. You'll collaborate with cross-functional teams including data scientists, product managers, and risk engineers to deliver data solutions that enable real-time fraud detection, personalized recommendations, credit scoring, and regulatory compliance reporting.

Key technical challenges include maintaining data consistency across distributed systems with demanding performance requirements, implementing comprehensive data quality frameworks with real-time validation, optimizing query performance on large datasets, and ensuring complete data lineage and governance across multiple business domains. At Paytm, reliable data streams are fundamental to our operations and our commitment to protecting customers' financial security and maintaining India's digital payments infrastructure.

Key Responsibilities

Data Stream Architecture & Development Design and implement reliable, scalable data streams handling high-volume transaction data with strong data integrity controlsBuild real-time processing systems using modern data engineering frameworks (Java/Python stack) with excellent performance characteristicsDevelop robust data ingestion systems from multiple sources with built-in redundancy and monitoring capabilitiesImplement comprehensive data quality frameworks, ensuring the 4 C's: Completeness, Consistency, Conformity, and Correctness - ensuring data reliability that supports sound business decisionsDesign automated data validation, profiling, and quality monitoring systems with proactive alerting capabilities Infrastructure & Platform Management Manage and optimize distributed data processing platforms with high availability requirements to ensure consistent service deliveryDesign data lake and data warehouse architectures with appropriate partitioning and indexing strategies for optimal query performanceImplement CI/CD processes for data engineering workflows with comprehensive testing and reliable deployment proceduresEnsure high availability and disaster recovery for critical data systems to maintain business continuity

Performance & Optimization Monitor and optimize streaming performance with focus on latency reduction and operational efficiencyImplement efficient data storage strategies including compression, partitioning, and lifecycle management with cost considerationsTroubleshoot and resolve complex data streaming issues in production environments with effective response protocolsConduct proactive capacity planning and performance tuning to support business growth and data volume increases

Collaboration & Leadership Work closely with data scientists, analysts, and product teams to understand important data requirements and service level expectationsMentor junior data engineers with emphasis on data quality best practices and customer-focused approachParticipate in architectural reviews and help establish data engineering standards that prioritize reliability and accuracyDocument technical designs, processes, and operational procedures with focus on maintainability and knowledge sharing

Required Qualifications

Experience & Education Bachelor's or Master's degree in Computer Science, Engineering, or related technical field

7+ years (Senior) of hands-on data engineering experience

Proven experience with large-scale data processing systems (preferably in fintech/payments domain)

Experience building and maintaining production data streams processing TB/PB scale data with strong performance and reliability standards

Technical Skills & RequirementsProgramming Languages:

Expert-level proficiency in both Python and Java; experience with Scala preferred

Big Data Technologies: Apache Spark (PySpark, Spark SQL, Spark with Java), Apache Kafka, Apache Airflow

Cloud Platforms: AWS (EMR, Glue, Redshift, S3, Lambda) or equivalent Azure/GCP services

Databases: Strong SQL skills, experience with both relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra, Redis)

Data Quality Management: Deep understanding of the 4 C's framework - Completeness, Consistency, Conformity, and Correctness

Data Governance: Experience with data lineage tracking, metadata management, and data cataloging

Data Formats & Protocols: Parquet, Avro, JSON, REST APIs, GraphQL Containerization & DevOps: Docker, Kubernetes, Git, GitLab/GitHub with CI/CD pipeline experience

Monitoring & Observability: Experience with Prometheus, Grafana, or similar monitoring tools

Data Modeling: Dimensional modeling, data vault, or similar methodologies

Streaming Technologies: Apache Flink, Kinesis, or Pulsar experience is a plus

Infrastructure as Code: Terraform, CloudFormation (preferred)

Java-specific: Spring Boot, Maven/Gradle, JUnit for building robust data services

Preferred Qualifications

Domain Expertise

Previous experience in fintech, payments, or banking industry with solid understanding of regulatory compliance and financial data requirementsUnderstanding of financial data standards, PCI DSS compliance, and data privacy regulations where compliance is essential for business operationsExperience with real-time fraud detection or risk management systems where data accuracy is crucial for customer protection

Advanced Technical Skills (Preferred)

Experience building automated data quality frameworks covering all 4 C's dimensionsKnowledge of machine learning stream orchestration (MLflow, Kubeflow)Familiarity with data mesh or federated data architecture patternsExperience with change data capture (CDC) tools and techniques

Leadership & Soft Skills Strong problem-solving abilities with experience debugging complex distributed systems in production environmentsExcellent communication skills with ability to explain technical concepts to diverse stakeholders while highlighting business valueExperience mentoring team members and leading technical initiatives with focus on building a quality-oriented cultureProven track record of delivering projects successfully in dynamic, fast-paced financial technology environments

What We Offer

Opportunity to work with cutting-edge technology at scaleCompetitive salary and equity compensation

Comprehensive health and wellness benefits

Professional development opportunities and conference attendanceFlexible working arrangements

Chance to impact millions of users across India's digital payments ecosystem

Application Process

Interested candidates should submit:

Updated resume highlighting relevant data engineering experience with emphasis on real-time systems and data quality

Portfolio or GitHub profile showcasing data engineering projects, particularly those involving high-throughput streaming systems

Cover letter explaining interest in fintech/payments domain and understanding of data criticality in financial services

References from previous technical managers or senior colleagues who can attest to your data quality standards

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The Role

We're looking for a senior AI engineer who can build production-grade agentic AI systems. You'll be working at the intersection of cutting-edge AI research and scalable engineering, creating autonomous agents that can reason, plan, and execute complex tasks reliably at scale.

What We Need

Agentic AI & LLM Engineering

You should have hands-on experience with:

Multi-agent systems : Building agents that coordinate, communicate, and work together on complex workflows

Agent orchestration : Designing systems where AI agents can plan multi-step tasks, use tools, and make autonomous decisions

LLMOps Experience : End-to-End LLM Lifecycle Management - hands-on experience managing the complete LLM workflow from prompt engineering and dataset curation through model fine-tuning, evaluation, and deployment. This includes versioning prompts, managing training datasets, orchestrating distributed training jobs, and implementing automated model validation pipelines. Production LLM Infrastructure - experience building and maintaining production LLM serving infrastructure including model registries, A/B testing frameworks for comparing model versions, automated rollback mechanisms, and monitoring systems that track model performance, latency, and cost metrics in real-time.

AI Observability : Experience implementing comprehensive monitoring and tracing for AI systems, including prompt tracking, model output analysis, cost monitoring, and agent decision-making visibility across complex workflows.

Evaluation frameworks : Creating comprehensive testing for agent performance, safety, and goal achievement

LLM inference optimization : Scaling model serving with techniques like batching, caching, and efficient frameworks (vLLM, TensorRT-LLM)

Systems Engineering

Strong backend development skills including:

Python expertise : FastAPI, Django, or Flask for building robust APIs that handle agent workflows

Distributed systems : Microservices, event-driven architectures, and message queues (Kafka, RabbitMQ) for agent coordination

Database strategy : Vector databases, traditional SQL/NoSQL, and caching layers optimized for agent state management

Web-scale design : Systems handling millions of requests with proper load balancing and fault tolerance

DevOps (Non-negotiable)

Kubernetes : Working knowledge required - deployments, services, cluster management

Containerization : Docker with production optimization and security best practices

CI/CD : Automated testing and deployment pipelines

Infrastructure as Code : Terraform, Helm charts

Monitoring : Prometheus, Grafana for tracking complex agent behaviors

Programing Language : Java , Python

What You'll Build

You'll architect the infrastructure that powers our autonomous AI systems:

Agent Orchestration Platform : Multi-agent coordination systems that handle complex, long-running workflows with proper state management and failure recovery.

Evaluation Infrastructure : Comprehensive frameworks that assess agent performance across goal achievement, efficiency, safety, and decision-making quality.

Production AI Services : High-throughput systems serving millions of users with intelligent resource management and robust fallback mechanisms.

Training Systems : Scalable pipelines for SFT and DPO that continuously improve agent capabilities based on real-world performance and human feedback.

Who You Are

You've spent serious time in production environments building AI systems that actually work. You understand the unique challenges of agentic AI - managing state across long conversations, handling partial failures in multi-step processes, and ensuring agents stay aligned with their intended goals.

You've dealt with the reality that the hardest problems aren't always algorithmic. Sometimes it's about making an agent retry gracefully when an API call fails, or designing an observability layer that catches when an agent starts behaving unexpectedly, or building systems that can scale from handling dozens of agent interactions to millions.

You're excited about the potential of AI agents but pragmatic about the engineering work required to make them reliable in production.

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