Selected experience
Selected experience. Measurable outcomes.
A look at complex data, cloud and AI challenges solved across enterprise environments — and the thinking we bring to every MercuryNova engagement.
Selected professional experience · Data platform & forecasting
Modernizing a large-scale enterprise forecasting platform.
A complex forecasting environment depended on data from more than 100 sources across multiple systems, creating challenges around scalability, processing time, reliability and governance.
Designed and led modernization of the data architecture using modern cloud and data engineering technologies. Simplified ingestion patterns, improved orchestration, strengthened governance and introduced scalable processing across the platform.
100+
Data sources integrated
~50%
Reduction in processing time
~40%
Reduction in infrastructure cost
Outcome — A more scalable and maintainable platform with faster processing, lower infrastructure cost and improved reliability.
Python · PySpark · Databricks · Snowflake · AWS · Azure · Airflow
Selected professional experience · Cloud modernization
Moving legacy data workloads to a modern cloud architecture.
Legacy infrastructure had become expensive to operate, difficult to scale and increasingly complex to maintain.
Redesigned the architecture around cloud-native storage, compute and data-processing patterns while maintaining continuity for downstream analytics and business users.
Lower infrastructure cost · Faster processing · Improved scalability · Simpler operational model
Outcome — Reduced infrastructure spend while significantly improving processing performance and creating a stronger foundation for future data workloads.
AWS · S3 · Glue · Athena · SQL · Python · Databricks
Selected professional experience · AI & engineering productivity
Using AI to accelerate engineering and knowledge workflows.
Technical teams were spending significant time navigating documentation, understanding legacy systems and performing repetitive engineering tasks.
Introduced AI-assisted engineering workflows using copilots, LLM APIs, retrieval-based systems and agentic concepts to improve how teams access information and complete technical work.
AI copilots · LLM integrations · RAG · AI agents · Knowledge retrieval · Developer productivity
Outcome — Created faster access to technical knowledge and demonstrated how AI-assisted workflows could improve engineering productivity and decision-making.
Our approach
The technology changes. The principles don’t.
Understand before building — business problem, existing architecture and operating constraints.
Simplify the architecture — remove unnecessary complexity before introducing new technology.
Design for production — scalability, reliability, governance and maintainability from the beginning.
Measure the outcome — connect technical improvements to operational or business results.
Your challenge
What are you trying to solve?
Whether you’re modernizing a data platform, introducing AI or rebuilding critical software, we can help define the architecture and move it into production.
Book a consultation