Top SQL Jobs in Pune

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Lead data analysis workstreams using large datasets and AWS analytics tools to define metrics, test hypotheses, perform root-cause analysis, produce insights and visualizations, and present actionable recommendations to stakeholders.

The Principal Consultant will design and implement data warehouse and BI solutions, integrating data from various sources, and lead client engagement throughout the process.

Develop and maintain Digital Workplace tools, working closely with teams, performing testing, debugging, and technical research. Requires web development experience and familiarity with modern software architectures.

As a Technical Account Manager, you'll bridge Hightouch and Fortune 5000 customers, ensuring onboarding, customer success, and increasing product adoption through exceptional technical management and communication skills.

The role involves back-end development using .NET Core and SQL, contributing to Jibble 2.0 API integrations, and collaborating with product and design teams.

Lead the modeling of the Customer Data Platform, defining event taxonomy and ensuring reliable datasets for analytics, product insights, and machine learning. Collaborate with various teams to maintain data consistency and shape the architecture of the behavioral data platform.

The Senior Data Engineer will design and maintain pipelines for event data ingestion and validation, ensuring operational reliability and consistency for analytics.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

The role involves designing, developing, and maintaining backend services for an Identity Security Platform, collaborating with cross-functional teams, and optimizing performance and security.

Lead engineering teams to deliver high-quality, scalable software. Provide technical guidance, mentor staff, manage timelines, risks and resources, improve engineering processes, enforce standards, collaborate with product and stakeholders, and support deployment pipelines and ML model deployment.

Analyze and interpret complex data to drive business decisions. Design and deliver reports and dashboards using SAP Fiori and SAP Analytics Cloud. Improve data quality via profiling, catalogs, ML techniques, and cleansing rules. Collaborate with stakeholders, document processes, mentor junior staff, and integrate analytics into governance and technology initiatives.

Design, develop and deliver scalable AWS-based data solutions and ETL pipelines using Python/PySpark, Snowflake, and AWS analytics services. Collaborate across teams, implement secure coding and unit testing, optimise data ingestion and transformations, and support project delivery and stakeholder engagement.

Analyze diverse data sources, build models and dashboards, perform descriptive/diagnostic analytics and data profiling, apply ML and AI tools to improve data quality, and deliver BI reports and governance guidance using SAP and Microsoft BI technologies.

Lead design, build and maintenance of data architectures, pipelines, warehouses and lakes; enable ML lifecycle by preparing governed datasets, collaborating with data scientists, and driving data governance, lineage, access controls and analytics-ready schema design.

Own end-to-end advertising performance for a book of marketplace accounts: strategy, targets, partner relationships, and optimization across Amazon Ads, DSP, and AMC. Surface risks and opportunities proactively, translate data into commercial decisions, mentor junior staff, manage budgets, and deliver against team and individual performance targets.

Develop and integrate automation and UI tools for certification programs (HLK, HDMI, DP, ESXi, Google xTS). Build multi-agent, AI/ML-enhanced solutions, work with certification kits, debug certification issues, improve processes, and coordinate cross-functional teams to ensure certification-level product quality.

Lead product strategy and execution for Communications within Lending. Define OKRs and roadmaps, collaborate with engineering, design, ML, risk and compliance, use data to drive decisions, and deliver omni-channel communication features that improve members' financial health.

As a Principal Product Manager, you'll define product strategy, lead cross-functional teams, drive product development, and ensure customer-centric solutions with a focus on financial services.

Own marketing tracking and measurement across 14 markets, implement and audit GTM/GA4/pixels/server-side tracking, build automated Looker dashboards, run incrementality and A/B/geo-lift tests, define budget allocation frameworks, and set analytics standards and documentation to inform media spend decisions.

Serve as a senior functional SME for Oracle Cloud and R12 financial modules (O2C, P2P, Projects). Own complex incidents, stakeholder communication, mentoring, configuration, testing, integrations, reporting (OTBI/BI Publisher), SQL-based analysis, quarterly updates/patching, and continuous improvement including AI-led productivity initiatives.

Join an agile Scrum team to design, develop, test, and deliver Windows enterprise software. Mentor junior engineers, write unit/integration tests, collaborate with product owners, estimate work, and use Azure DevOps.

Design, develop, and maintain Power BI dashboards and semantic models using Snowflake as the data warehouse. Build efficient SQL queries, DAX calculations, implement RLS, optimize performance, schedule refreshes, and collaborate with stakeholders to translate requirements into scalable BI solutions.

Design, build, and operationalize ML and deep learning solutions including supervised/unsupervised models and LLM fine-tuning. Implement RAG, vector DBs, prompt engineering, deploy on cloud (AWS/Azure), set up experiments, and present technical findings while partnering with business stakeholders.