Top SQL Jobs in Pune

Build and support production MLOps pipelines on GCP using Vertex AI, Airflow, Kubeflow, and related tools. Responsibilities include feature engineering, model deployment, monitoring, retraining, lifecycle management, performance optimization, CI/CD, documentation, and operational support. The role collaborates with data science, data engineering, cloud platform, and MLOps teams to deliver scalable machine learning solutions and establish reusable MLOps standards and best practices.

Leads a first-line compliance function for liquidity regulatory reporting. Responsibilities include designing compliance test cases, managing testers, executing and analyzing testing, identifying regulatory or policy non-compliance, coordinating remediation with Finance Change and Reporting Operations, and communicating outcomes to senior stakeholders. The role requires expertise in LCR, NSFR, maturity ladders, banking products, regulatory requirements, SQL, ETL transformations, and complex data analysis.

Design, develop, and deploy production-grade generative AI and agentic systems, including RAG pipelines, autonomous agents, prompt architectures, and enterprise integrations. Build solutions using LangChain, LangGraph, vector databases, cloud services, APIs, and DevOps tooling. Advise stakeholders, document and monitor AI applications, mitigate technical and security risks, modernize RPA assets, and mentor junior developers.

Designs and implements web and API test automation frameworks, migrates automation to Playwright, integrates tests into CI/CD, and measures product quality. The role defines testing standards, develops functional UI and service-level tests, maintains testing infrastructure, conducts peer reviews, manages risks, documents strategies, reports quality outcomes, mentors team members, and advises QA teams. Requires strong JavaScript, testing methodologies, database, cloud DevOps, version control, and Agile expertise, with four hours of overlap with Central US hours.

Leads client-facing delivery of complex data, analytics, AI, cloud, and modern data platform solutions. Owns technical workstreams from discovery through deployment, designs pipelines and integrations, modernizes legacy data environments, applies testing and governance practices, troubleshoots complex issues, and ensures production readiness. Guides engineers, reviews technical deliverables, contributes to proposals and technical discovery, mentors consultants, and develops reusable engineering patterns for the Data Practice.

Analyze sales, financial, customer, and commercial performance data to support forecasting, business planning, growth opportunity identification, and executive decision-making. Build dashboards, reporting solutions, forecasts, scenarios, and actionable recommendations. Partner with business and technology stakeholders to improve data quality, KPI governance, analytics adoption, and measurement of commercial initiatives. Apply statistical, predictive, machine learning, and experimentation techniques when they deliver measurable business value.

Responsible for fixing product and security bugs, improving product supportability, debugging complex issues, writing quality Java code, developing Java/JavaScript UI components, integrating REST services, collaborating with support/professional services for L3 escalations, and working with databases, app servers, and monitoring/deployment tools.

Senior full-stack .NET developer responsible for designing, developing, integrating and deploying web applications. Lead technical delivery, mentor teams, implement CI/CD, unit tests, and collaborate with stakeholders. Work with .NET Core, C#, Azure, Angular, SQL and ORMs while promoting ownership, Agile practices, and strong OO design.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Supports liquidity regulatory reporting controls by documenting data flows, business data elements, lineage, transformations, and controls from booking systems to reporting platforms. Performs SQL-based data analysis, test-case development, functional and business testing, quality remediation, root-cause analysis, requirements management, stakeholder coordination, and management reporting. Contributes to implementation activities, change control, documentation standards, and training junior team members.

Leads full-stack design, development, debugging, testing, and maintenance for Vertiv’s cloud platform supporting remote services. Owns features across the stack, develops reusable components and prototypes, collaborates on architecture and interfaces, estimates work, documents solutions, and mitigates technical risks. The role requires strong React.js, .NET Core, C#, C++, SQL, web services, database, messaging, and IoT experience, with additional exposure to cloud, Docker, microservices, and Python beneficial.

Develop and maintain production software for a cloud-native distributed SQL database. Responsibilities include designing, coding, testing, and deploying new and existing projects involving query optimization, fault tolerance, server architecture, concurrency, multithreading, LLMs, vector indexes, SIMD, and LLVM code generation. Engineers collaborate across teams, own projects end to end, and contribute throughout the product lifecycle.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Leads end-to-end WMS and LMS implementation projects across warehouse sites, covering requirements, design, configuration, integrations, testing, launch, documentation, training, troubleshooting, and production support. Manages vendors, clients, budgets, risks, schedules, communications, and project closure. Develops WMS customizations and business requirements, coordinates cross-functional teams, mentors junior staff, and ensures successful, on-time deployments.

Leads the organization’s data science and AI roadmap, managing a hands-on team that develops statistical models, experiments, forecasting, segmentation, production AI solutions, and agentic workflows. Drives GCP-based model deployment, MLOps/LLMOps, governance, observability, ethical AI, and adoption of AI tools. Partners with business, engineering, product, BI, and analytics teams to deliver practical data products, define success metrics, and communicate technical insights to executives.

Develop, test, deploy, and support enterprise integration solutions using MuleSoft Anypoint Platform and Mule 4.x. Build System, Process, and Experience APIs; create REST/SOAP integrations; transform data with DataWeave; implement security, logging, monitoring, and error handling; write MUnit tests; troubleshoot production issues; and support CI/CD collaboration with technical and business teams.

Entry-level QA trainee responsible for creating and executing test cases, performing functional, regression, smoke, sanity, and integration testing, documenting defects, retesting fixes, and validating applications across browsers and devices. The role involves collaboration with developers and analysts, test documentation, and learning API, database, and automation testing using modern QA tools.

Lead full-stack software architecture and development, with a frontend emphasis, across scalable backend services and frontend microapps. Drive technical design, reliability, DevOps, CI/CD, and cloud-native engineering; mentor engineers and collaborate across distributed teams. Build AI-powered platform features using LLMs, agents, and related frameworks. Improve React performance, service reliability, incident response, and engineering effectiveness while integrating customer-focused solutions across multiple domains.

Configure Fenergo KYC and client lifecycle management solutions for capital markets clients. Responsibilities include workflow, rules, policy, risk assessment, ownership modeling, API integrations, data mapping and validation, SaaS configuration, release management, defect investigation, and root-cause analysis. The role also supports testing, DevOps practices, cloud integration, identity management, and enterprise security patterns.

Develop and operate backend software in a cross-functional product team, contributing throughout the engineering lifecycle from design and coding through testing, deployment, and support. Build scalable microservices and maintain high standards for code quality, security, and performance. Required experience includes Java, Spring, GCP, Terraform, Docker, Kubernetes, PostgreSQL, Cassandra, and automated testing and deployments. Collaborate with engineers and clients to solve complex technical and business problems.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.

Owns data quality, governance, metadata, taxonomy, and operational excellence across diverse content domains. The role establishes standards, monitors quality metrics, investigates root causes, builds dashboards and documentation, and drives scalable process improvements. It partners with Content Operations, Product, Engineering, and Quality teams to resolve systemic issues, support automation, handle customer escalations, and improve datasets for customer and AI use cases.

Designs and executes functional, regression, integration, UI, and API testing; develops automation frameworks; tracks and analyzes defects; integrates tests into CI/CD pipelines; validates production issues and hotfixes; collaborates with developers on fixes; participates in design reviews; maintains QA documentation; and improves testing processes, coverage, reliability, and release confidence.

Own the CRM Opportunities backend end to end, designing scalable data models, APIs, distributed workflows, event-driven services, and background jobs. Improve performance and reliability across large multi-tenant pipelines while handling consistency, caching, sharding, observability, and incident response. Build AI-assisted CRM features using existing LLM APIs, including context assembly, response handling, cost and latency controls, and graceful degradation. Occasionally ship Vue.js UI changes and operate services through deployment and on-call.