Top SQL Jobs in Pune

The Power BI Developer will connect with business stakeholders, analyze data, recommend solutions, and act as a bridge between business and tech teams, focusing on data modeling and visualization best practices.

The Bigdata Engineer will design and develop ETL/Hadoop and analytics components, ensuring performance optimization and adherence to architectural standards, while managing distributed computing tasks and data ingestion workflows.

Lead the development of analytics systems and predictive models, manage a team, preprocess data, analyze trends, and present findings.

We are looking for a Data Engineer with skills in Python, SQL, and Azure technologies to develop Big Data applications. Responsibilities include working with Spark and Docker to handle large scale data processing and analytics.

The Big Data Engineer will support existing applications and work on new design and implementation requirements, utilizing big data technologies and tools.

The Data Engineer role involves analyzing raw data, building data systems, conducting complex analyses, and enhancing data quality by collaborating with data scientists and architects.

The ETL Developer is responsible for creating ETL packages, managing SQL databases, performance tuning, and automating data processes while ensuring data integrity and quality.

The role involves expertise in Teradata architecture, SQL performance tuning, and end-to-end data warehousing implementation, with knowledge of Azure tools being advantageous.

This role involves collaborating with users to build data solutions, managing AWS data technologies, and automating workflows for efficiency.

The Java Automation Engineer will develop automation frameworks, maintain scripts, and collaborate with teams in an Agile environment, utilizing tools like Selenium and Java.

The Power BI Developer will create datasets, dashboards, and reports, collaborate with users to meet requirements, maintain the Power BI environment, and provide user support and training.

The role requires developing ETL processes, integrating data sources, and writing complex SQL queries. Additionally, experience with data warehousing concepts and Big Data technologies is needed.

The Azure Data Engineer will implement big data pipelines using Azure technologies, collaborate with stakeholders, and utilize tools like Data Factory and Databricks.

Lead or support Infor SyteLine ERP deployments, configure modules (Manufacturing, Supply Chain, Finance, Inventory, CRM), develop customizations using .NET and SQL, integrate with non-Infor systems, and build dashboards and reports. Requires understanding of manufacturing and supply chain processes.

Design and implement scalable cloud-native data and analytics platforms (AWS/Azure) using Databricks, Snowflake, SQL, Python/PySpark. Build ingestion, ETL/ELT, orchestration, integration, and reporting pipelines. Support data modeling, analytics-ready structures, BI tools, CI/CD, deployments, production support, incident management, and AI/ML-enabled analytics. Collaborate with architects, stakeholders, and engineering teams to drive standards, governance, and operational excellence.

Design and implement AI/LLM-driven data extraction and ETL pipelines, build web crawling and API integrations, implement RAG systems, mitigate LLM hallucinations, evaluate third-party tools, and collaborate with content and analytics teams to deliver high-quality, scalable data for downstream analysis.

Lead tax functional consultant responsible for implementing and supporting global indirect tax solutions using Vertex and Oracle Cloud Fusion Tax. Partner with business stakeholders to define processes and roadmaps, configure tax rules, manage integrations, support SIT/UAT, troubleshoot critical issues, and lead cross-functional teams through implementations and M&A-related tax integrations.

Develop, test, debug and maintain web and mobile fintech applications. Work in an Agile environment with cross-functional teams, manage time-sensitive deliverables, provide technical support, and improve user experience while reporting to IT leads and the CTO.

Develop and maintain Python backend components, design and optimize SQL for large datasets, perform ETL, integrate front-end and third-party services, troubleshoot applications and databases, ensure data quality and security, participate in code reviews, and produce technical documentation.

Design and implement Python-based automated UI and API tests using Selenium, Pytest, Rest Assured or Postman. Validate data with SQL, maintain test environments, collaborate with development and QA, analyze results, and report defects to ensure high-quality deliveries.

Deploy and productionize ML models on GCP Vertex AI for real-time and batch inference. Build scalable pipelines, perform feature engineering, monitor model performance and drift, support ML-Ops and governance, and collaborate with teams on retail banking (credit risk, marketing, customer service) use cases.

Lead design and delivery of NLP and Generative AI solutions, from problem definition through deployment. Build and fine-tune transformers and LLMs for summarization, QA, classification and chatbots, apply classical and deep learning methods, ensure scalable reproducible ML workflows in cloud environments, mentor junior data scientists, and collaborate with product and engineering to translate business problems into AI solutions.

Design, build, and maintain scalable web applications using the MERN stack and Next.js. Collaborate with cross-functional teams and designers to implement UI/UX, create reusable components, optimize performance across devices, manage and improve codebases, work with SQL databases, integrate RESTful APIs, and troubleshoot complex issues.

Lead delivery of data and digital consulting services: define deliverables, engage senior stakeholders, manage small teams, design scalable data pipelines, implement dashboards and analytics, ensure data quality and governance, and drive digital transformation using cloud, BI, and automation tools.

Audit claim and denial work for accuracy across human and AI outputs, build and maintain QA scorecards and dashboards, design rule-based quality checks, identify root causes and coach billing teams, and surface reproducible AI error patterns to Product and Engineering.