Data & Analytics

Data engineering and business analytics

Paul Titov & Co builds data pipelines, shared metric definitions, analysis, and reporting for product, finance, and growth teams. We reconcile sources and make data quality visible before building dashboards, so the result supports decisions rather than producing another version of the numbers.

Data work earns its place when it improves a decision. That requires more than a dashboard: definitions have to be agreed, sources reconciled, pipelines made observable, and the result presented at the level where a team can act on it.

We build collection and transformation pipelines, analytical models, product and marketing measurement, forecasting, anomaly detection, and decision-focused reporting. The work can begin with a fragmented reporting stack, a product that needs its first measurement model, or a specific commercial question that existing data cannot answer reliably.

Because analytics sits next to engineering and growth in the same team, implementation does not stop at a recommendation. Event schemas, database models, attribution paths, dashboards, and the product changes needed to improve the signal can be delivered as one system.

Pipelines, modelling, and data quality

We collect data from applications, databases, advertising platforms, payment systems, files, and third-party APIs; clean and transform it; and deliver it into analytical models and reporting systems. Pipelines are designed with validation, retries, monitoring, lineage, and clear ownership of definitions.

Database work includes schema design, indexing, query performance, replication, analytical stores such as ClickHouse, Elasticsearch or OpenSearch, and vector retrieval where the use case requires it. Python, Pandas, NumPy, SciPy, scikit-learn, and notebooks support analysis, forecasting, segmentation, and reproducible investigation.

Product, revenue, and marketing analytics

Product measurement can include event schemas, funnels, cohorts, retention, activation, feature adoption, segmentation, and experiment analysis. Commercial work can include revenue, payments, subscriptions, customer value, forecasting, and anomaly detection. Marketing analytics connects campaign cost, attribution, conversion, and downstream value across channels.

Reporting can be delivered through Looker Studio, Power BI, custom interfaces, or automated summaries. Related applications include fintech products, e-commerce platforms, and the measurement layer behind performance marketing.

From disputed numbers to decisions

The first step is agreeing what a number means. “Revenue,” “active customer,” or “successful order” often has several definitions across finance, product, and marketing. We trace each metric back to its source, document the business rule, assign ownership, and make late events, refunds, duplicates, time zones, and historical changes explicit. Only then do we automate the pipeline and build a dashboard.

The result can include a warehouse model, tested transformations, scheduled imports, data-quality alerts, governed metric definitions, role-specific reporting, and self-service exploration. We design for investigation as well as presentation: a team should be able to move from an unusual chart to the accounts, orders, campaigns, or system events behind it. Documentation and lineage make the system maintainable when products and providers change.