Jobs at Lingaro

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Lead data modeling for SAP domains including Supply Chain, Order-to-Cash, and Finance. Design and review conceptual, logical, and physical models; validate mappings, data structures, business rules, and data contracts; and ensure governance, quality, metadata, lineage, and canonical model alignment. Use Databricks and SQL to support integration, transformation, reporting, and analytics. Collaborate with data, engineering, SAP, architecture, and business stakeholders to resolve modeling gaps and approve production-ready designs.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

The Data Scientist will implement GenAI systems, build components, support project delivery, analyze findings, and deliver technical solutions, focusing on NLP and deep learning.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Develop end-to-end classification and forecasting models, including data preparation, exploratory analysis, feature engineering, model training, tuning, evaluation, visualization, and deployment support. Collaborate with data and AI engineers on productionization, monitoring, dashboards, and reproducible documentation. Apply Python, SQL, classical machine learning, time-series modeling, cloud platforms, and business-focused analytics to solve problems such as demand forecasting and churn prediction.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Designs and maintains automated UI, API, integration, and data tests using Playwright, TypeScript or JavaScript, and related frameworks. Builds scalable automation frameworks, validates databases, ETL processes, data pipelines, and data quality, and works with Azure environments. Defines testing strategies, identifies quality risks, improves reliability and coverage, integrates tests into CI/CD pipelines, and uses AI tools to improve test creation, analysis, and debugging.

Business Analyst gathers and governs requirements, maps business processes and data flows, defines KPIs and data-quality rules, maintains data catalogs and metadata, and advises stakeholders on solutions. The role translates requirements into functional specifications, user stories, and acceptance criteria; leads UAT planning, defect triage, and business sign-off; and coordinates testing, deployment, and implementation. Experience with SAP, cloud data platforms, dbt, data lineage, APIs, and AI-assisted delivery tools is advantageous.

Build enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry. Develop topics, triggers, routing, fallback handling, generative answers, RAG, knowledge grounding, citations, metadata-based retrieval, multilingual capabilities, and prompts. Integrate agents with Power Automate, SharePoint, Dataverse, Teams, Azure AI Search, and Azure OpenAI or Foundry models. Support troubleshooting, security, environments, ALM, and DEV/UAT/PROD deployments.

Lead business analysis for procurement-focused BI projects. Gather stakeholder requirements, document business processes, create functional specifications and data mappings, and define Power BI dashboards, data models, calculations, and visualizations. Support project planning, scope and milestone tracking, RAID and decision logs, governance communications, UAT, deployment, training, and hypercare. The role requires strong Power BI, data modeling, DAX, Azure, ETL/ELT, procurement analytics, and cross-functional stakeholder management experience.

Design, develop, and maintain Python backend services, RESTful APIs, and scalable microservices. Deploy and manage Azure cloud solutions, CI/CD pipelines, automated testing, Docker, and Kubernetes environments. Collaborate in Agile/Scrum teams through code reviews, planning, and architecture discussions. Apply database, data modeling, cloud architecture, scalability, security, and performance optimization knowledge, with potential work involving Databricks, Golang, React, infrastructure as code, and event-driven systems.

Designs and evolves an Azure-based observability and data quality platform. Builds reliable, automated infrastructure using IaC, CI/CD, Python, and Shell scripting; integrates AKS, Kafka, Databricks, Flink, Fivetran, and Azure SQL Server; develops monitoring and dashboards with Grafana, Prometheus, Power BI, or Databricks; applies SRE and FinOps practices to improve reliability, performance, data quality, incident response, compliance, and cloud cost efficiency.

The role involves shaping Data Management strategy, leading solutions development, driving AI innovation, building collaborations, and coaching junior team members.

Design, develop, and deploy enterprise Power Platform solutions, including Copilot Studio agents, multi-agent architectures, RAG pipelines, Power Apps, Power Automate workflows, Dataverse models, integrations, and ALM processes. Troubleshoot production issues, implement governance and security, collaborate with business analysts, mentor junior developers, and lead technical design discussions. The role also involves prompt engineering, Azure AI integrations, and staying current with Power Platform and generative AI capabilities.

Design, develop, and maintain server-side components and APIs for scalable web applications. Manage relational/NoSQL databases, integrate third-party APIs, implement security best practices, optimize performance, collaborate with front-end and QA, use Git, and produce documentation.

Build and maintain React/Next.js applications with high performance and responsiveness. Collaborate with cross-functional teams in Agile ceremonies, identify and fix bugs, maintain code quality, implement responsive designs, and write tests for web applications. Share knowledge via workshops and support offshore and stakeholder-driven development.

Senior supply chain consultant to support presales, RFPs, requirements analysis, solution design, and delivery. Lead discovery workshops, fit-gap analysis, process mapping, and stakeholder management to define scalable, analytics- and AI-enabled planning and execution solutions across ERP and planning platforms.