ANALYTICS ENGINEERING. AI-READY DATA.
Data pipelines, trusted models, MCP integrations, and AI agents. Built around your business, with deliberate control over data and actions.
ERWork directly with Etiosa RichmoreAnalytics Engineer · Malmö, Sweden01 / EXPERTISE
Start with the part that needs attention. A focused data problem or the architecture connecting it all.
A reliable foundation for every question.
Turn operational data into clear, tested models. Define business logic once, trace it back to the source, and give analytics a foundation people can understand.
Discuss this serviceKeep the right data moving.
Connect APIs, databases, and business systems. Design ingestion, transformations, and orchestration with monitoring and recovery paths built in.
Discuss this serviceUseful access. Deliberate boundaries.
Prepare the data layer behind AI. Classify sensitive fields, design masking and access rules, and make the boundary between useful context and unnecessary exposure explicit.
Discuss this serviceYour data. A controlled connection to AI.
Build Model Context Protocol (MCP) servers that connect AI tools to your business data. Prepare and model the underlying datasets, define the tools and resources exposed, and apply authentication and row-level security where required.
Discuss this serviceUseful automation. Clear operating limits.
Build agents for defined business tasks, with explicit limits on the data and tools they can use. Deploy in an isolated environment, such as a dedicated VPS, with scoped credentials and approval steps for sensitive actions.
Discuss this service02 / SELECTED THINKING
A short example from my recent analytics engineering work. The full reasoning is there if you want to go deeper.
Masking a customer name can still leave it exposed in a derived deal label. The fix starts upstream, with classification and the dependencies behind the field.
Customer classification across historical records, warehouse-level masking, semantic-layer controls, and validation of downstream data exposure. Review and testing informed the implementation decisions.
The derived label still contains a personal name.
Preserve useful context while reducing exposure.
Synthetic records, reconstructed from my published explanation. These are not client data or production metrics.
03 / APPROACH
Practical engineering starts with the decisions the system needs to support.
Understand your systems, business logic, and constraints. Establish what is trustworthy and where the gaps are.
Agree on a focused scope, clear responsibilities, and checks that show whether the solution does what it should.
Make changes reviewable. Test the important assumptions and document the decisions so your team can own what comes next.
04 / THE PERSON BEHIND R DATABLOCKS
I'm an analytics engineer with a background in finance and on-chain analytics. I work on the data foundations behind numbers people can trust—and the boundaries behind useful AI access.
Through R DataBlocks, you work directly with me. I bring the same attention to business definitions, edge cases, and reviewable engineering decisions that I write about at Behind the Query.
Explore my technical writing05 / START A CONVERSATION
A pipeline to improve, data to connect through MCP, or a task to automate with an AI agent. Tell me what you have in mind.
etiosa@rdatablocks.comYour enquiry comes directly to me.
