Top SQL Jobs in Pune

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.

Designs and develops AI and machine learning models, deploys them into scalable production applications through APIs, microservices, and cloud platforms, and trains models on large datasets. Responsibilities include NLP, computer vision, testing, debugging, optimization, and collaboration with data scientists and software engineers. Requires proficiency in programming, machine learning frameworks, cloud and data tools, and AI development techniques.

Develop and maintain Azure Databricks data pipelines using PySpark, Spark SQL, SQL, and Delta Lake. Support Informatica-to-Databricks modernization and HR data integrations, build source-to-target mappings and dimensional models, perform profiling, testing, reconciliation, and defect resolution, and create technical documentation. Apply data governance and access controls through Unity Catalog, participate in agile ceremonies and code reviews, and escalate risks while working under senior direction.

Designs and deploys scalable Azure Databricks data pipelines using PySpark, Spark SQL, and Delta Lake across medallion layers. Builds HR data models, source-to-target mappings, reconciliation controls, governance, lineage, documentation, and data quality processes. Modernizes legacy Informatica and SQL integrations, including Oracle HCM feeds. Collaborates with distributed agile teams, mentors junior engineers, and supports production cutovers for sensitive employee data.

Develop and optimize Databricks ETL/ELT pipelines using PySpark, SQL, Delta Lake, and Python. Translate client business problems into technical designs, lead project modules, manage delivery tasks, mentor team members, and collaborate with architects, analysts, and clients. Responsibilities include data governance, documentation, solution architecture, testing, deployment planning, risk mitigation, and delivery of technology solutions in a hybrid, client-facing consulting environment.

Designs, builds, and maintains scalable batch and real-time data pipelines across Azure and on-premises environments. Develops data lakes, warehouses, marts, and Medallion architectures using Spark and modern big data technologies. Ensures pipeline reliability, performance, security, governance, privacy, and data quality while collaborating with business, technology, analytics, DevOps, and data governance teams. Supports platform modernization and continuous improvement.

Designs and delivers scalable data engineering solutions across Azure and on-premises environments. Builds batch and real-time ETL/ELT pipelines, data lakes, warehouses, marts, and streaming platforms using Spark and related Big Data technologies. Leads architecture, technical design, optimization, governance, and end-to-end implementation while ensuring quality, security, reliability, and performance. Collaborates with cross-functional stakeholders, mentors data engineers, and communicates technical recommendations to business and technology audiences.

Leads the design, development, and maintenance of enterprise-scale batch and real-time data pipelines across Azure and on-premises environments. Builds scalable ingestion and processing frameworks, optimizes reliability and performance, and ensures data quality, governance, security, and compliance. Provides solution architecture, technical leadership, stakeholder collaboration, mentoring, and engineering standards adoption across complex programs.

Design, develop, test, deploy, and support secure, scalable payment-network software using Java, Spring Boot, microservices, REST APIs, Kafka, and event-driven architecture. Troubleshoot production and performance issues, contribute to CI/CD, observability, resiliency, and automation, and collaborate across engineering, product, architecture, and operations teams. Provide technical leadership, mentor team members, and drive decisions across workstreams.

Lead reliability-focused technical programs across Mastercard’s authentication platforms. Coordinate Agile ceremonies, resource planning, backlog prioritization, dependencies, release governance, platform upgrades, migrations, and production rollouts. Partner with Engineering and SRE teams to improve availability, resiliency, performance, scalability, operational readiness, observability, automation, and disaster recovery. Manage risks and stakeholder communication while influencing cross-functional teams and driving continuous operational improvement.

Provides technical leadership for teams developing secure, scalable e-commerce payment authentication products. Leads complex software projects, architecture, Agile delivery, technical solution evaluation, quality assurance, security, budgets, and cross-functional collaboration. Oversees cloud-native microservices development, mentors engineers, supports talent acquisition, improves engineering standards, and ensures delivery within schedule and budget. The role requires extensive software engineering leadership, AI-native development, mobile application experience, payment-domain knowledge, and expertise in Java, Spring, cloud platforms, and security technologies.

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.

Lead presales technical activities for SailPoint identity security solutions, including customer discovery, solution design, technical presentations, demonstrations, evaluations, and RFx responses. Partner with sales, customers, and channel teams to communicate identity security value, address technical requirements, support deal closure, and influence product roadmaps. The role also includes product certification, customer success content, internal enablement, and mentoring newer team members.

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.

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.

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.

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.

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.

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.

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.

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.