Expertise & credibility
The work combines hands-on engineering, certified cloud knowledge, careful automation and end-to-end monitoring, so systems stay useful long after launch. Here is the stack we actually work in day to day, what we have been vetted on, and the environments that shaped our standards.
Tools & technologies
Python and SQL pipelines into BigQuery, Snowflake, Amazon Redshift or PostgreSQL, split into raw, staging, curated and reporting layers. Incremental loads, scheduled refreshes, automated backfills, Parquet extracts and query tuning. Includes cross-chain transaction data decoded with contract ABIs and Web3.py.
Fact and dimension models with an agreed grain, conformed dimensions and curated SQL views behind every dashboard. Reporting in Power BI, Tableau, Looker Studio, Streamlit and Plotly, plus review of existing SSAS or Analysis Services models and Salesforce CRM Analytics datasets, recipes and dataflows.
Google Cloud, AWS and Azure services for storage, compute, orchestration and warehousing: BigQuery, Cloud Run, Cloud Logging, Secret Manager and Apps Script; S3, EC2 and Redshift; Azure Data Factory, Azure SQL, Blob Storage and Data Lake ingestion. Scheduled jobs, containers, monitoring and reviewable deployments.
TypeScript, JavaScript and Python behind responsive interfaces, client portals, admin panels and internal tools. REST APIs, OAuth flows and webhooks into business systems, plus Streamlit and Plotly data apps with filterable tables, date ranges and live record counts when the audience is internal.
Retrieval-augmented generation over a defined knowledge set, Model Context Protocol servers in Python, custom AI connectors, plugins and reusable AI skills, and agentic workflows that retrieve context, generate SQL, validate the result and stop for approval. GPT, Claude, Llama and Qwen integrated behind your own access rules.
Validation and reconciliation frameworks comparing record counts, date coverage, nulls, duplicates, totals and metrics between stages and systems. Metadata profiling, dependency and lineage analysis, outlier detection and alerting, and access control with OAuth and managed secrets that supports SOC 2 programmes.
Who we work with
Whether you are validating a first product or modernising a large reporting estate, the engineering standards are the same: traceable data, controlled access, documentation and monitoring.
First data and product infrastructure built with scale in mind but without premature complexity: one warehouse, clear definitions, a dashboard people believe.
Systems that handle increasing complexity, operational load and reporting needs without a rewrite, including the reconciliation and data quality checks that growth exposes.
Migrating legacy databases and manual reporting into Snowflake or BigQuery, rebuilding semantic and BI layers, and adding specialist capacity alongside internal teams.
Our engineers have worked with
Directly or through vetted talent platforms. These names describe experience, not endorsements or current partnerships.
Credentials
Put it to the test
You will speak with the engineers who would do the work. Bring your stack, your constraints and the problem that keeps coming back.
