Top SQL Jobs in Pune

Lead consulting engagements that translate business requirements into technical solutions. Design and deliver data platforms, ETL pipelines, analytics solutions, architectures, prototypes, testing, deployment, and operations plans using Databricks, Snowflake, Python, PySpark, SQL, and AWS. Manage project teams, distribute tasks, mentor junior staff, communicate with clients, support project planning and risk mitigation, and ensure high-quality delivery across global teams.

Build and deliver client technology solutions using ETL platforms, SQL, data warehousing, programming, and cloud technologies. Own assigned activities across project lifecycles, apply Agile or Waterfall methodologies, support requirements, architecture, prototyping, testing, and operations, and assist with project management. Collaborate with global teams and clients while applying creative, analytical, and consulting-oriented problem solving.

Collaborates with internal and client teams to analyze business problems, translate requirements into technical designs, and implement technology solutions. Develops technical features using appropriate methodologies, testing practices, and technology stacks. Supports project implementation planning, interprets business requirements, and contributes to small- and medium-scale client engagements involving databases, programming, cloud platforms, and analytics.

Develop and maintain Oracle database solutions for high-availability payment platforms. Responsibilities include complex PL/SQL development, SQL performance tuning, data modeling, migration, quality, ETL, production support, automated database deployment, and Agile delivery. The role collaborates with distributed engineering teams to deliver reliable enterprise systems and continuous improvements.

Leads software engineering for Mastercard Send, owning complex service-level problems, architecture, secure coding, testing, integration, automation, and production reliability. Writes and reviews code, drives prioritization and agile delivery, improves CI/CD pipelines, conducts troubleshooting and incident postmortems, mentors engineers, guides architecture, supports product acceptance, and participates in hiring and promotion decisions. The role requires cross-functional collaboration across globally distributed teams and ownership of high-availability digital payment services.

Develops, tests, validates, and supports large-scale batch and real-time data platforms using Spark, Kafka, and NiFi. Builds resilient data pipelines, validation frameworks, distributed storage solutions, deployment automation, and operational tooling. Optimizes performance, scalability, and reliability; supports production troubleshooting and incident resolution; contributes to architecture, CI/CD, documentation, code reviews, and engineering standards; and mentors junior engineers.

Own delivery of shared data models and pipelines, lead migration from Hadoop and on-premises ETL to Databricks, manage cross-team dependencies, oversee vendors, maintain legacy platform commitments, and establish planning and reporting practices for a new technical team.

Lead the design and development of scalable cloud-native data platforms and batch or real-time pipelines supporting AI, machine learning, and Generative AI. Build infrastructure for feature engineering, model training, inference, LLMs, RAG, embeddings, and vector databases. Ensure data quality, governance, security, observability, reliability, and cost efficiency while enabling MLOps and AgenticOps capabilities across enterprise AI initiatives.

Owns the vision, roadmap, backlog, requirements, and launches for data platform products. Translates business and customer needs into technical features, user stories, and acceptance criteria; partners with engineering and technical program teams; monitors product performance, adoption, and operational health; manages documentation, prioritization, and post-launch feedback. The role also supports data quality, governance, analytics, stakeholder collaboration, mentoring, and hiring activities.

Design, build, and deploy AI and Generative AI solutions using LLMs, agent frameworks, and RAG. Develop scalable AI/ML pipelines, cloud-native integrations (Azure/Databricks/AWS), and MLOps/LLMOps deployments. Integrate AI with APIs, databases, and internal apps. Implement Responsible AI practices (bias detection, hallucination mitigation, explainability, safety guardrails). Monitor, test, and maintain production AI systems while staying current on Generative AI trends.

Leads the design and development of scalable Java backend applications using Spring Boot, microservices, and REST APIs. Provides architectural and technical leadership, mentors engineers, drives coding standards, and oversees initiatives from design through production support. Collaborates with product, architecture, QA, DevOps, and engineering teams while resolving complex technical issues and performance bottlenecks. Preferred experience includes payments or fintech, Kafka, databases, cloud platforms, Kubernetes, security, CI/CD, and mobile application backends.

Design and develop a scalable automated securities finance platform using Java, Spring, microservices, distributed systems, containers, SQL, AWS, messaging, and caching technologies. Build enterprise applications, optimize high-volume transactional data models and queries, integrate with existing workflows, and collaborate with developers, analysts, QA, and operations teams.

Lead Software Engineer responsible for developing, maintaining, and supporting high-availability payment platforms. The role involves Java backend development, Oracle database work, REST and event-driven integrations, production reliability, Agile delivery within SAFe, distributed team collaboration, testing, automation, and continuous improvement. Experience with Spring Boot, Angular, JavaScript, SQL, DevOps tools, and security mechanisms is required; payment or financial-services experience is preferred.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.

Build and maintain scalable batch and streaming data pipelines, ingestion processes, curated datasets, and data models for analytics and data products. Support data quality monitoring, documentation, governance, security, troubleshooting, code reviews, testing, and CI/CD. Collaborate with analysts, product owners, developers, and DevOps teams while using Azure services, Python or comparable programming languages, SQL, Spark, Databricks, Synapse, and orchestration tools.

Develops, tests, implements, documents, maintains, and supports secure, scalable software applications. Conducts technical research, feasibility studies, system design, debugging, incident management, and production support. Works within agile and waterfall environments, participates in on-call rotations, uses automated testing and continuous delivery practices, and supports software quality, metrics, documentation, and process improvement.

Owns reporting and analytics for Mastercard’s Product Express platform. Translates stakeholder needs into scalable dashboards, metrics, and reporting solutions; monitors adoption and performance; analyzes data; and recommends operational improvements. The role also leads onboarding digitization, prioritizes platform enhancements, and identifies AI applications such as anomaly detection, automated insights, natural-language querying, and proactive recommendations.

Leads client-facing data engineering engagements from discovery through production, translating business requirements into cloud architectures, ETL/ELT pipelines, data lakes, warehouses, and analytics solutions. Oversees technical delivery, project planning, quality reviews, testing, documentation, and stakeholder communications. Mentors engineers and consultants, drives coding and data quality standards, supports business development, and develops scalable solutions using Python, SQL, Spark, and major cloud platforms. The role also contributes to AI-enabled data engineering and supply chain or manufacturing solutions.

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.

Design, develop, and maintain scalable cloud-based applications using Java and Spring Boot. Collaborate with product and engineering teams, follow security and architectural standards, and participate in testing, code reviews, deployments, incident response, and root-cause analysis. Improve CI/CD pipelines, automation, documentation, and engineering workflows while adopting AI-assisted development tools. Contribute to Agile planning, technical research, project estimation, and continuous learning.

Build and scale production-grade agentic AI infrastructure and platform capabilities. Lead architecture, technical design, reliability, scalability, data privacy, and end-to-end delivery. Collaborate with product and innovation teams to define customer solutions, mentor engineers, improve developer experience, and resolve complex technical issues. The role requires backend engineering, cloud infrastructure, APIs, databases, automated testing, infrastructure as code, and CI/CD expertise.

Build applied AI systems and prototypes using LLMs, RAG pipelines, vector databases, agentic workflows, structured extraction, and custom models. Design evaluation frameworks, measure accuracy and grounding, analyze errors, and iterate on solutions. Work with large structured and unstructured datasets using Python, SQL, and cloud data platforms. Partner with product, data, engineering, and business teams to translate ambiguous problems into measurable AI solutions and document architectures, prompts, runbooks, and results.

Designs and implements automated test frameworks and Java-based automation for API, UI, integration, regression, and end-to-end testing. Integrates tests into Jenkins CI/CD pipelines, performs defect and root-cause analysis, defines test strategies, and collaborates across development, product, and DevOps teams. The role also involves Agile participation, quality engineering improvements, and mentoring junior engineers.

Design, develop, test, deploy, and support scalable full-stack applications using Java, Spring Boot, React, microservices, REST, gRPC, messaging, and relational databases. Own initiatives from requirements through production, contribute to architecture and technical decisions, build automated tests using TDD, improve CI/CD and engineering productivity with AI-assisted tools, troubleshoot production issues, conduct code reviews, and mentor engineers while collaborating across distributed teams.