Top SQL Jobs in Pune

Performs data profiling, validation, quality testing, anomaly detection, pipeline monitoring, and quality reporting across cloud and on-premises environments. Builds validation rules and data quality artifacts, collaborates with business and technology stakeholders, leads solution design and estimation, and mentors Data Quality Analysts. Works with data lakes, data warehouses, datamarts, and enterprise data platforms.

Build and maintain cloud or on-premises ETL/ELT pipelines for large-scale structured and unstructured data processing. Ensure data quality, security, and compliance while collaborating with business, technology, operations, governance, architecture, data science, DevOps, and BI teams. The role requires expertise in big data frameworks, Azure services, distributed databases, data warehouses, data marts, and data lakes.

The Data Analyst develops production-grade data and analytics solutions by translating business needs into functional requirements, analyzing and validating data, and contributing to architecture, governance, modeling, quality, engineering, and data science initiatives. The role independently leads solution design and estimation, collaborates with business and technology stakeholders, mentors analysts, and applies SQL, big data, cloud, and data storage expertise to solve complex business problems.

Support and enhance SAP Concur Expense and Travel systems, including configuration, reporting, API integrations, and boundary-system connectivity through Boomi. Gather requirements, design and develop secure, testable solutions, create Cognos reports, provide ongoing application support, and collaborate with business stakeholders and project teams. Ensure compliance with development methodologies, security standards, estimates, testing, documentation, and Mastercard policies.

Leads the design, development, and maintenance of scalable batch and real-time data pipelines across Azure and on-premises environments. Builds production-grade ETL/ELT solutions, optimizes performance and reliability, ensures data quality and compliance, and solves complex business problems. Collaborates with business and technology stakeholders, leads solution design and estimation, and coaches data and analytics talent.

Supports technical project delivery through requirements gathering, functional and technical specifications, system behavior definition, API and data modeling, integration analysis, gap analysis, testing documentation, and application troubleshooting. Collaborates with stakeholders, development teams, vendors, and business owners to translate business needs into implementable solutions. The role also supports risk management, incident management, Agile delivery, and reliable operation of enterprise systems.

Leads and mentors software engineering teams developing scalable Java applications and microservices for high-volume card payment platforms. Owns architecture, design quality, cloud modernization, Agile delivery, operational excellence, security, and production reliability. Manages hiring, performance, career development, risks, dependencies, and cross-functional coordination while solving complex payment processing and settlement issues.

Leads customer-embedded AI discovery and rapid proof-of-concept development for issuer and acquirer clients. Builds agentic pipelines, conversational interfaces, and analytical tools to validate business hypotheses, gathers customer feedback, and translates findings into evidence-backed product briefs and roadmap recommendations. Partners with product and engineering teams, identifies repeatable platform opportunities, supports pre-sales discussions, and mentors engineers and analysts on customer discovery and PoC scoping.

Leads software engineering efforts supporting billing operations and business systems. Designs, integrates, tests, and supports billing platforms, APIs, microservices, SaaS, ERP, CRM, ETL, and database-driven systems. Applies strong programming, SQL, data modeling, automation, security, and DevOps practices while partnering with finance, product, operations, customer success, and engineering stakeholders. Mentors team members, improves processes, translates business requirements into technical outcomes, and ensures accurate invoicing, reconciliation, reporting, and system quality.

Design, develop, and maintain scalable full-stack software, microservices, APIs, database solutions, and enterprise applications for Mastercard’s B2B payment platform. Collaborate with product, architecture, quality engineering, and DevOps teams throughout the Agile lifecycle. Participate in architecture reviews, code reviews, testing, production support, incident resolution, monitoring, performance optimization, and continuous improvement.

Owns platform product vision, roadmap, feature prioritization, requirements, releases, metrics, operational health, and customer-impacting issues. Partners with engineering, UX, technical program managers, business stakeholders, vendors, and customer service. Drives platform launches, documentation, PCI audits, vulnerability and technical-debt tracking, performance analysis, and continuous improvements while mentoring team members.

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.

Manages software engineering teams and owns end-to-end delivery of analytics and business intelligence solutions. Responsibilities include people management, technical strategy, architecture and system design, code and deliverable reviews, quality and security oversight, technical debt management, innovation, stakeholder coordination, and Agile delivery. The role requires expertise in React.js, .NET, AWS, databases, Hadoop, Spark, Databricks, big-data analytics, CI/CD, DevOps, and automation.

Leads multiple software engineering teams developing secure, scalable ecommerce payment authentication solutions. Oversees strategy, staffing, budgets, processes, project delivery, quality, compliance, and technical direction. Requires extensive software engineering leadership and EMVCo 3DS payments experience, with strong expertise in Java, Spring, cloud platforms, microservices, security, and modern development practices. Partners across global technology, business, and external provider teams while mentoring staff and driving continuous improvement.

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.

Build and improve production-oriented enterprise AI solutions using prompts, LLMs, RAG, workflow automation, APIs, governed data, and evaluation frameworks. Integrate systems such as Salesforce, Slack, Snowflake, and AWS using n8n, Python, JavaScript, or TypeScript. Apply context engineering, observability, security, privacy, testing, and reliability controls while partnering with business, product, data, security, and engineering stakeholders.

Leads enterprise data quality strategy, architecture, platforms, frameworks, observability, governance, and automation. Designs reusable quality capabilities for batch, streaming, cloud, on-premises, and SaaS environments; establishes rules, metrics, alerts, and remediation workflows; integrates quality with metadata, lineage, catalogs, MDM, and data contracts; mentors engineering teams; evaluates tools; and applies AI and machine learning to anomaly detection, root-cause analysis, and trusted AI-ready data platforms.

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

Lead software engineer responsible for designing, developing, testing, deploying, and maintaining scalable Java-based software solutions. Owns the full software development lifecycle, including requirements, architecture, test strategy, performance benchmarking, release planning, production support, incident management, and root-cause remediation. Evaluates technologies, contributes to proof-of-concepts, promotes quality and process improvements, and collaborates with globally distributed development teams.

Leads software engineering projects, develops complex software solutions, researches technical alternatives, and guides daily development activities. Responsibilities include programming, proof-of-concept development, roadmap creation, quality and process adherence, third-party evaluation, and collaboration with business and technical stakeholders. Requires strong Java-stack expertise, relational database and SQL skills, software design knowledge, Agile experience, and the ability to lead distributed teams.

Designs, builds, maintains, monitors, and optimizes scalable ETL/ELT pipelines for structured and unstructured data across Azure and on-premises environments. The role supports batch and real-time processing, data quality, security, compliance, and performance tuning. It collaborates with business, technology, operations, governance, architecture, data science, DevOps, and BI teams to deliver production-grade data and analytics solutions.

Leads the design, development, deployment, and support of scalable data pipelines and platforms across Hadoop, on-premises, and AWS cloud environments. Builds batch and near-real-time processing frameworks, modernizes ETL and data warehouse workloads, implements governance and observability capabilities, and troubleshoots production issues. Mentors engineers, collaborates with cross-functional stakeholders, evaluates emerging technologies, and ensures secure, high-quality delivery for enterprise-scale data ecosystems.

Build and maintain petabyte-scale data pipelines, entity-centric data models, and distributed workflows using Databricks and Spark. Partner with data science teams to productionize ML features and artifacts, optimize ETL/ELT processes, and implement testing, monitoring, observability, and incident response. Collaborate with governance and operations teams to ensure secure, compliant data management while supporting scalable merchant identity and trust-profile systems.

Build and enhance Ethoca products across the full software development lifecycle, including design, development, testing, deployment, configuration, and documentation. Develop scalable test automation frameworks, review quality solutions, promote shift-left practices, and contribute to architecture and design decisions. Collaborate with product and engineering teams, manage risks, integrate services, mentor engineers, and improve operational metrics. Work with microservices, distributed systems, cloud-native applications, APIs, CI/CD platforms, and data validation.

Build and operate production LLM systems, including prompt and pipeline versioning, evaluations, monitoring, observability, RAG and vector-store infrastructure, safety guardrails, and cost governance. Develop CI/CD and operational tooling for non-deterministic AI outputs, manage reliability and compliance requirements, and collaborate with engineering and product teams to turn AI prototypes into scalable, secure, cost-effective production systems.