Top SQL Jobs in Pune

Design, develop, and optimize scalable big data applications and ETL/ELT pipelines using Scala, Apache Spark, and distributed data technologies. Build reusable processing frameworks, tune Spark jobs, troubleshoot production issues, implement data-quality monitoring, and collaborate with technical and business teams. Apply software engineering practices including testing, code reviews, Git, and CI/CD while working with Hadoop, HDFS, Hive, Kafka, and Spark SQL.

Design, develop, optimize, and support scalable batch and real-time data pipelines using Kafka, Spark, Scala/PySpark, SQL, Python, and orchestration tools. Build reliable ETL/ELT workflows, optimize distributed processing, manage cloud data lake storage, troubleshoot production incidents, perform root cause analysis, conduct code reviews, and mentor junior engineers. Independently deliver complex data engineering solutions with emphasis on scalability, performance, reliability, and maintainability.

Designs, develops, and maintains scalable batch and streaming data pipelines using Python, Scala, Spark, and Databricks. Responsibilities include optimizing large-scale data workloads, building ETL/ELT processes, modeling data warehouses and lakes, implementing data quality and governance standards, troubleshooting production issues, and collaborating with architects, analysts, and data scientists.

Develop and test high-performance software for Mastercard’s real-time decision management and fraud prevention platform. Drive quality engineering practices, automate acceptance, integration, functional, and non-functional testing, and improve CI/CD and release processes. Collaborate with developers to build reliable, performant, and testable systems using Java, microservices, cloud technologies, and observability tools. Mentor team members while remaining hands-on in development and test automation.

Leads the Payment Reconciliation Intelligence program across product and engineering teams. Responsibilities include managing complex initiatives, technical program managers, Agile/SAFe ceremonies, Scrum teams, roadmaps, budgets, quality metrics, risks, stakeholders, and delivery trade-offs. The role requires technical understanding of product architecture, hands-on experience with .NET or Java, cloud and microservices exposure, and strong program leadership, communication, and problem-solving skills.

Develops and productionizes machine learning and data science solutions for Mastercard’s Business & Market Insights products. Responsibilities include feature engineering, model training and deployment, exploratory analysis, large-scale PySpark processing, advanced SQL, pipeline optimization, responsible AI controls, and product innovation. Partners with product, engineering, sales, and clients to prioritize solutions, deliver milestones, incorporate feedback, and improve products. Provides technical leadership and mentorship to junior data scientists.

Develop, test, deploy, configure, and document secure, scalable software for Mastercard’s authentication and payment security products. Own delivery tasks, collaborate with distributed teams, support software metrics, improve processes, and mentor junior engineers. The role requires Java and Spring Boot expertise, database and web-stack experience, CI/CD implementation, testing practices, and familiarity with cloud, microservices, payment security, and Agile methodologies.

Develop advanced reporting, data visualization, data models, and analytical solutions using large-scale datasets. Translate business requirements into deliverables, build repeatable modeling and reporting processes, manipulate databases, and provide actionable insights through presentations. Apply data validation and cleansing, while developing generative AI solutions with Python, Azure OpenAI, LLMs, agentic systems, and workflow automation.

Build and productionize AI-powered applications, backend services, RAG solutions, MCP integrations, and agentic workflows. Translate requirements into secure, scalable software; establish AI evaluation and validation standards; implement testing, CI/CD, observability, resilience, privacy, compliance, and Responsible AI controls. Collaborate with cross-functional teams to deliver reliable enterprise solutions in an Agile environment.

Develop and maintain scalable cloud-based applications using Java and Spring Boot. Collaborate with product and engineering teams, implement microservices and APIs, ensure security and code quality, and support testing, deployment, monitoring, and incident remediation. Contribute to CI/CD automation, Agile ceremonies, technical documentation, proof-of-concepts, and AI-assisted software development practices. Work with relational and NoSQL databases, containers, DevOps tools, and emerging AI technologies.

Develops and productionizes machine learning and data science solutions for Mastercard’s Business & Market Insights products. Responsibilities include feature engineering, model training, evaluation, deployment, exploratory analysis, large-scale data processing with PySpark, advanced SQL, and scalable ML workflows. The role partners with product, engineering, client services, and sales teams to deliver analytics-driven products, evaluates AI/ML approaches, drives product improvements, and mentors junior data scientists.

Lead design and build cloud-native batch and real-time data platforms. Develop scalable ETL/ELT and streaming pipelines (Spark, Kafka, Flink), ensure data quality, governance, observability, security, cost optimization, and drive platform engineering excellence and team mentorship.

Leads data science and AI/ML product development, designing, productionizing, and optimizing traditional machine learning and generative AI solutions. Builds LangChain and LangGraph applications, processes large datasets with PySpark and SQL, evaluates model quality and performance, and applies privacy, security, responsible AI, monitoring, and explainability controls. Partners cross-functionally on product strategy, mentors data scientists, establishes reusable technical practices, and drives releases and continuous product improvement.

Designs, builds, tests, and maintains scalable full-stack analytics applications, responsive user interfaces, data models, APIs, and data pipelines. Partners with product managers and designers to deliver customer-focused features, improves development processes, reviews code, mentors junior engineers, and collaborates across global teams. The role requires strong Java and modern web development expertise, cloud-native experience, distributed systems knowledge, automated testing skills, and proficiency in agile software engineering.

Designs, develops, supports, and optimizes scalable data pipelines and distributed processing workloads. Writes production-grade Python, PySpark, and SQL; tunes Spark and SQL performance; troubleshoots Linux and AWS infrastructure; improves monitoring, reliability, security, and cost efficiency; performs root cause analysis; and collaborates with engineering, product, architecture, and operations teams. The role also supports AI-enabled data workflows using LLMs, Amazon Bedrock, Agentic AI, and MCP.

Design and maintain Java-based test automation frameworks for Spring Boot microservices. Own API, integration, regression, and end-to-end testing, including Kubernetes deployment validation, migration testing, database verification, contract testing, and CI/CD integration. Validate resilience, asynchronous processing, regional configurations, service integrations, and production readiness. Collaborate with development and platform teams to resolve defects and improve automation coverage, reliability, and quality.

Leads the design, modernization, development, and deployment of Java microservices applications. Provides technical direction to backend and SDET teams, designs REST APIs and distributed integrations, manages Kubernetes deployments and CI/CD pipelines, and coordinates regional releases. Responsibilities include code and architecture reviews, performance optimization, observability, secure coding, automated testing, production troubleshooting, incident management, and stakeholder coordination.

Leads architecture and design for scalable, highly available payment services. Responsibilities include creating high-level and low-level designs, developing critical components, preparing proofs of concept, conducting code reviews, mentoring engineers, documenting designs, managing source control operations, evaluating technologies, and improving engineering processes. The role requires expertise in Java, Spring, databases, NoSQL, messaging, microservices, cloud deployment, testing, Agile practices, security concepts, and enterprise application design.

Supports technology projects through requirements gathering, technical discovery, functional specifications, API and data modeling, integration analysis, testing documentation, application troubleshooting, and stakeholder collaboration. The role translates business needs into implementation-ready designs, performs functional gap analysis, supports Agile delivery, manages risks, and coordinates with development teams, vendors, and business stakeholders across enterprise insurance systems.

Develop, test, deploy, troubleshoot, and refactor application software components. Collaborate on requirements analysis, prioritization, estimation, and technical decisions while adopting new languages and architecture patterns. Support CI/CD automation, pipelines, virtualization, TDD, BDD, and Agile development. Assist teammates, communicate effectively, demonstrate analytical problem-solving, and maintain information security responsibilities.

Designs and develops high-performance API platforms for transaction data using Java, microservices, and distributed systems. Leads architecture, technical roadmaps, system development, maintenance, troubleshooting, performance optimization, and delivery against SLAs. Guides engineers, conducts technical interviews, coaches team members, and collaborates globally. Requires expertise in Spring, SQL and NoSQL databases, cloud hosting, automated testing, and CI/CD practices.

Develop and support full-stack applications using Java, Spring Boot, React, and TypeScript. Design REST APIs and microservices, integrate relational databases, secure applications with OAuth2/OIDC, and deploy containerized solutions on Kubernetes and Azure. Build automated tests, CI/CD pipelines, observability, and cloud-ready distributed systems. Troubleshoot across application layers, document architectural decisions, make engineering trade-offs, and mentor junior engineers.

Leads quality assurance and software testing for large-scale card payment authorization systems. Develops test strategies, plans, automation, regression assessments, and release validation; translates requirements into test methods; performs functional, system, UAT, and integration testing; manages defects and root-cause analysis; mentors team members; and collaborates with developers, business teams, and payment-domain experts.

Designs, builds, and operates production-grade agentic AI systems using Python, LLM orchestration, tool calling, retrieval, memory, multimodal models, and LLMOps. The role develops scalable backend services, observability, RAG and Graph-RAG systems, model evaluation, and responsible AI safeguards. It also mentors engineers, establishes GenAI engineering standards, and productionizes emerging AI capabilities across enterprise applications.

Build and operate production-grade agentic AI systems, including tool-calling layers, Python backend services, multi-agent orchestration, memory, retrieval, observability, LLMOps, and multimodal pipelines. Develop RAG and Graph-RAG systems, fine-tune and evaluate foundation models, implement responsible AI controls, and mentor engineers. The role requires strong backend engineering, API integration, cloud, distributed systems, and production LLM application experience.