Projects

Many industries rely on SafetyCulture API to conduct inspections, track compliance, and manage safety risks. However, extracting and integrating this data efficiently into business intelligence systems was a challenge. Our goal was to develop an automated pipeline that fetches inspection data from the SafetyCulture API and processes it for reporting and analytics. To achieve this, we built a data extraction and transformation workflow that pulls real-time data, cleans it, and stores it in a structured format. This solution allows organizations to identify safety risks, monitor compliance trends, and generate actionable insights, ensuring a proactive approach to workplace safety and operational efficiency.

Supply chain faced the critical challenge of managing products with Best Before Dates (BBD). Each product had a specific timeframe before it could no longer be sold to third parties. To avoid potential losses, it was imperative to track the "Sell by Maximum Time" (SMT) date, the latest date a product could be sold. Our goal was to develop an algorithm that identifies the count of products at risk of exceeding their SMT date based on current stock and sales forecasts.

This project focused on automating the CI/CD pipeline to improve deployment efficiency and data quality in a cloud-based environment. By implementing Bitbucket Pipelines, the team enabled automated deployments for Cloud Composer, Dataflow, and DBT projects, reducing manual effort and minimizing errors. Additionally, unit tests and data validation were integrated into the pipeline to ensure reliability. The setup also included automated environment configurations for development and production, streamlining testing and deployment processes. Comprehensive documentation and training were provided to support the transition, resulting in faster, more reliable deployments and improved data processing efficiency.

By integrating Danone’s ERP data with logistics provider datasets, this project provided a comprehensive view of logistics costs, allowing for detailed cost breakdown analysis. The solution empowered Danone to slice and dice logistics expenses, identify cost fluctuation root causes, and optimize spending. Through custom KPI development, the company gained better control over supply chain expenditures, leading to significant cost savings and enhanced operational efficiency. The implementation ensured data-driven decision-making, helping Danone proactively manage logistics costs and improve overall performance.

A leading FMCG company launched a structured training program to enhance data literacy across customer teams, enabling better data access and decision-making. The program was divided into three levels: Report Viewers learned to navigate pre-built reports, Medium Users created custom reports with integrated data, and Pro Users received advanced training in data architecture and SQL. This approach empowered teams to work independently with data, reducing reliance on IT support. As a result, reporting became more efficient, decision-making faster, and data interpretation more accurate, contributing to the company’s digital transformation efforts.

The Market Share Cockpit is a powerful analytics solution that consolidates and enriches sell-out data from Nielsen and IQVIA, providing a unified view of market performance. By integrating external intelligence with internal ERP reports, it enables accurate comparisons and deeper consumer insights. Automated data quality checks, KPI analysis, and hierarchical transformations ensure data accuracy and consistency across all reporting levels. This solution empowers businesses to track trends, optimize strategies, and make data-driven decisions with confidence.

The Sales Analysis to Grow project delivers an advanced analytics solution that empowers businesses to gain a deeper understanding of their sales performance. By integrating and transforming data from multiple sources, the solution enables detailed analysis of customer behavior and product trends across different regions. Through interactive dashboards, users can uncover hidden growth opportunities, compare key performance indicators, and track market dynamics with precision. The solution ensures high data accuracy and consistency, providing a solid foundation for strategic decision-making and sales optimization.

Managing SharePoint data manually is challenging, especially when integrating it into modern cloud storage. This project delivers a secure and automated solution using Microsoft Graph API to extract data from SharePoint and store it in Azure Storage. The process starts with secure authentication, where a service principal secret is retrieved from Azure Key Vault to generate a bearer token for accessing Graph API. The necessary drive ID is then retrieved, and Azure Data Factory orchestrates the data transfer, ensuring seamless and scalable data ingestion. This solution enhances security with protected authentication, automates data flow to reduce manual effort, and provides a scalable, efficient pipeline for handling complex data integration tasks.

The customer faced performance constraints despite increasing Premium capacity, experiencing slow dataset refreshes and delayed visual rendering in Power BI reports. To address these issues, we conducted a comprehensive performance assessment, identifying and optimizing high CPU-consuming reports and inefficient data models. By implementing pushdown optimization, refining Power Query processing, and redesigning data models based on best practices, we accelerated data refresh times and visual response speeds. Additionally, we provided targeted Power BI training and real-time monitoring dashboards, enabling proactive issue detection and ensuring long-term performance sustainability.

GfK, a leading market research company, provides critical consumer goods data. However, manually accessing and integrating this data was cumbersome and inefficient. Our solution automates the entire workflow, enabling secure authentication, automated file retrieval, and streamlined data ingestion. The process begins with SAML authentication, where users log in via GfK Federation, redirecting them to GfK Connect for validation. To automate this step, we use Python’s Mechanize library to handle credential submission securely with secrets stored in Azure Key Vault. Once authenticated, our system performs automated file discovery using BeautifulSoup, identifying available data files. Data ingestion is handled by a Databricks Compute Cluster, which securely integrates the retrieved data into a Data Lake, preventing redundant processing.

To support Alpro’s Leadership Team, we designed a tailored EIS dashboard that aggregates and visualizes essential financial, logistical, and commercial metrics in a single, intuitive interface. By providing real-time insights into business performance, the system enables trend identification, proactive decision-making, and strategic planning. This data-driven approach enhances operational efficiency, ensures data consistency across departments, and optimizes decision-making processes, ultimately driving business performance and profitability.

SAP data for a Sales Order is often distributed across multiple tables (up to 15), requiring complex joins that increase CPU usage and error risks. This project streamlines data integration into a structured architecture to improve efficiency. Raw SAP data from key tables (VBAK, VBUP, VBKD, etc.) is extracted, cleansed, validated, and integrated to consolidate sales, delivery, and billing documents while ensuring incremental updates for performance optimization. By applying KPI-specific logic and business rules, the data is transformed into optimized formats, enabling advanced analytics and reporting. This structured approach simplifies SAP data retrieval, enhances data quality, and improves decision-making.

Employees constantly analyze the market and the Company's place in it. Looking for niches for new products and opportunities to expand the presence of existing ones. To accomplish these tasks, they need an analytical platform that will save them from routine data preparation processes and allow them to focus only on market analysis.