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Job Description:
We foster an intense work culture with a can-do attitude. All of our key managers come from excellent educational backgrounds and have significant experience growing a company and mentoring software engineers. Due to our smaller size, we adopt the latest technologies and computing trends ahead of the larger industry players. As a part of the company’s globally distributed engineering team, our engineers gain exposure to the latest software engineering practices and fast development cycles. Our developers routinely work on challenging technical problems that utilize the latest technologies for fast-paced software delivery. Examples of some of our projects:
One of the largest food and beverage companies in the world faced challenges due to unreliable data that did not give executives a real view of their customers’ points of sale. This resulted from having disparate systems that generated siloed, static, and conflicting reports. In addition, reporting performance was low and dashboards were not intuitive. We addressed these challenges by migrating multiple Business Intelligence platforms into Power BI, which reduced critical decision-making from weeks to days, brought savings in licensing and infrastructure, and led to skyrocketing Power BI adoption. This was accomplished through a well-crafted plan that included a Power BI migration and optimization, process automation through Power Automate, and a pilot to integrate Azure Synapse to infuse AI-driven insights into inventory, delivery routes, and sales forecasts. A key contributor to user adoption was our approach to implement Centers of Excellence to train hundreds of team members around the world in Power BI capabilities, governance, and best practices.
We worked on a Machine Learning solution for retail stores and helped them to increase their sales. We have worked with our clients to gather their sales data, promotion, and discount data for model engineering. We also used weather data from the US government to develop a historical weather model. Our team created a hybrid ML model that predicts future sales far more accurately than the existing ML model used by customers. We used Fourier transforms to decompose sales, analyze trends, and remove noise from the sales time series. Then we applied logarithmic, exponential, and S-curve transformations to features which helped us to develop hybrid regression models and predict upcoming future sales. Our Sales Forecasting Engine, built on Microsoft Azure Databricks, allowed our client to align their business objectives with predicted sales.
For another client, we developed a Business Intelligence solution to pull high volume data (more than 150 million rows) from source tables to a staging environment efficiently. We developed visually rich reports that allow business users to analyze the data from a different perspective and do what-if analysis based on business needs. We developed data orchestration pipelines to pull a high volume of data in 30 minutes and developed reports using Power BI with optimal page performance.
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