Dylan S.

Your entire data stack, owned by one engineer. From raw sources to AI agents: extraction, pipelines, warehousing, modeling, dashboards, MCPs, and Agents. One accountable system, zero handoff gaps. 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐭 𝐩𝐚𝐢𝐧 𝐩𝐨𝐢𝐧𝐭𝐬 𝐈 𝐫𝐞𝐬𝐨𝐥𝐯𝐞: 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 𝐖𝐞 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚, 𝐛𝐮𝐭 𝐰𝐞 𝐝𝐨𝐧’𝐭 𝐤𝐧𝐨𝐰 𝐡𝐨𝐰 𝐭𝐨 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐢𝐭 𝐨𝐫 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝, 𝐚𝐧𝐝 𝐰𝐞 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐦𝐚𝐤𝐢𝐧𝐠 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬”: vision and scope definition grounded in proven standards but adapted to the business reality and prioritized by expected value, full infrastructure setup from scratch (extraction, modeling, distribution and consumption), dashboards and deterministic systems such as alerts and notifications, and activation through automations, workflows, agents and MCPs. 𝐃𝐚𝐭𝐚 𝐲𝐨𝐮 𝐜𝐚𝐧’𝐭 𝐭𝐫𝐮𝐬𝐭 “𝐎𝐮𝐫 𝐀𝐈 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐨𝐮𝐫 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐜𝐭 𝐨𝐧 𝐝𝐚𝐭𝐚 𝐧𝐨𝐛𝐨𝐝𝐲 𝐡𝐚𝐬 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞𝐝”: reliable extraction, cleaning and modeling with a semantic layer, consumption-optimized data marts with RLS, and deterministic system design where AI is used only where it adds value, cutting unnecessary load and the probability of error. “𝐄𝐯𝐞𝐫𝐲 𝐝𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭 𝐫𝐞𝐩𝐨𝐫𝐭𝐬 𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐧𝐮𝐦𝐛𝐞𝐫 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐦𝐞𝐭𝐫𝐢𝐜, 𝐚𝐧𝐝 𝐧𝐨𝐛𝐨𝐝𝐲 𝐭𝐫𝐮𝐬𝐭𝐬 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐲𝐦𝐨𝐫𝐞”: exhaustive audit, repair and remodeling, plus a global semantic layer (e.g. dbt + OpenMetadata) as the single documented source of truth for definitions and metrics. 𝐁𝐫𝐨𝐤𝐞𝐧, 𝐬𝐥𝐨𝐰 𝐨𝐫 𝐢𝐧𝐡𝐞𝐫𝐢𝐭𝐞𝐝 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 “𝐎𝐮𝐫 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐛𝐫𝐞𝐚𝐤𝐬 𝐞𝐯𝐞𝐫𝐲 𝟏𝟐 𝐝𝐚𝐲𝐬 𝐚𝐧𝐝 𝐰𝐞 𝐤𝐞𝐞𝐩 𝐩𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐟𝐢𝐱𝐢𝐧𝐠 𝐢𝐭”: full end-to-end repair, with observability and traceability to catch failures before they reach the business. “𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐭𝐚𝐤𝐞 𝐦𝐢𝐧𝐮𝐭𝐞𝐬 𝐭𝐨 𝐥𝐨𝐚𝐝 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦 𝐡𝐚𝐬 𝐬𝐭𝐨𝐩𝐩𝐞𝐝 𝐰𝐚𝐢𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐭𝐡𝐞𝐦”: optimization of the model, warehouse or consumption layer, or migration to an optimal architecture without losing or corrupting data. “𝐓𝐡𝐞 𝐩𝐞𝐫𝐬𝐨𝐧 𝐰𝐡𝐨 𝐛𝐮𝐢𝐥𝐭 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐥𝐞𝐟𝐭 𝐚𝐧𝐝 𝐧𝐨𝐛𝐨𝐝𝐲 𝐤𝐧𝐨𝐰𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬”: reverse engineering and forensics of the pipeline, model and consumption/distribution layer, with restorations, repairs and migrations without rebuilding from scratch. 𝐒𝐜𝐚𝐭𝐭𝐞𝐫𝐞𝐝 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐢𝐧𝐟𝐥𝐚𝐭𝐞𝐝 𝐜𝐨𝐬𝐭𝐬 “𝐇𝐚𝐥𝐟 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦 𝐬𝐩𝐞𝐧𝐝𝐬 𝐭𝐡𝐞𝐢𝐫 𝐝𝐚𝐲 𝐜𝐨𝐩𝐲𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐟𝐢𝐥𝐞𝐬, 𝐞𝐦𝐚𝐢𝐥𝐬 𝐚𝐧𝐝 𝐏𝐃𝐅𝐬 𝐛𝐲 𝐡𝐚𝐧𝐝”: deterministic extraction from every source, with AI digestion only where the input is unstructured. “Between licenses, warehousing, queries and agents, it costs more than hiring someone”: determinism wherever possible, query and storage optimization, cost constraints, memory and context management for agents, and safe migration to open source. 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐜𝐡𝐚𝐧𝐠𝐞 “𝐖𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐨𝐨𝐥𝐬, 𝐛𝐮𝐭 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦 𝐬𝐭𝐢𝐥𝐥 𝐥𝐢𝐯𝐞𝐬 𝐢𝐧 𝐄𝐱𝐜𝐞𝐥 𝐚𝐧𝐝 𝐝𝐨𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐨𝐥𝐝 𝐰𝐚𝐲”: adoption management, deliverables designed for non-technical end users, documentation of manual rules, and handling ambiguity in processes. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗧𝗼𝗼𝗹𝗯𝗼𝘅 󠁯•󠁏󠁏 BI & Viz: Power BI, Looker Studio, Zoho Analytics, Metabase, Apache Superset, Domo 󠁯•󠁏󠁏 Modeling & Query: SQL, DAX, Power Query (M), Python, dbt, Excel 󠁯•󠁏󠁏 Warehousing: BigQuery, Snowflake, Cloud SQL, Cloud Storage, SQL Server, MySQL, Postgress, SQL Server, Amazon Redshift, Amazon S3 󠁯•󠁏󠁏 Automation & Integration: Make, n8n, Apps Script, Claude Run, 󠁯•󠁏󠁏 Pipelines & ETL: Fivetran, Supermetrics, Windsor AI, Coupler AI, Airbyte 󠁯•󠁏󠁏 Cloud & Governance: Google Cloud Platform, AWS, Azure, Terraform, Git, Open Metadata 󠁯•󠁏󠁏 AI Agents and Orchestation: Claude Code, GPT Codex, Grok, Gemini, Kimi 󠁯•󠁏󠁏 Other Languages: Java Script, HTML, CSS I make sure every byte moves, transforms, lands, and speaks, without friction, without hallucinations, and without AI needing to re-invent the wheel.