Selected Work

Projects

A selection of data & analytics projects where I built infrastructure, standardized reporting, and enabled data-driven decisions.

Bling data infrastructure
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01
ClickHouse Python Metabase Mixpanel

Designing and Implementing the Company's First Data Infrastructure

Bling  ·  Data infrastructure, automation, subscription analytics, KPI design

Challenge

Bling lacked a centralized data infrastructure. Reporting relied on Mixpanel and spreadsheets, metrics differed between teams, and manual workflows slowed decision-making. There was no unified view of subscriptions, revenue, or product performance.

Approach

I designed and implemented the company's first BI foundation using ClickHouse as a central warehouse. Python-based automations and API integrations streamlined data ingestion and eliminated manual reporting. Event tracking and subscription data were consolidated into a consistent data model, supported by a shared KPI framework across marketing, product, and leadership.

Outcome

A fully automated reporting layer in Metabase powering marketing dashboards, A/B testing analysis, subscription tracking, and executive reporting. Teams moved from fragmented reports to a reliable single source of truth, enabling faster and more aligned decision-making.

Marketing funnel project
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02
Snowflake dbt Segment Looker

Establishing a Unified Analytics Framework Across European Markets

heycar  ·  Data modeling standardization, multi-market reporting, analytics governance

Challenge

At heycar, reporting structures differed across markets, data models were fragmented, and definitions were inconsistent. Scaling decision-making across countries required a unified foundation.

Approach

I led the transition toward a standardized BI architecture, including migration to dbt for structured modeling and documentation. Market-specific reporting was consolidated into a centralized reporting layer for executive and operational use.

Outcome

The organization gained a harmonized analytics foundation enabling consistent cross-market reporting. Definitions were standardized, governance improved, and leadership gained reliable insights to support strategic decisions across European markets.

Executive reporting project
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03
Snowflake dbt Tableau

Executive Reporting & Metric Governance

[Company Name]  ·  Metric governance, executive reporting, data standardization

Challenge

[Placeholder] A Series A company had five people producing five different versions of the same KPIs. Leadership meetings regularly started with 15 minutes of reconciling conflicting numbers. There was no single definition of revenue, churn, or active users.

Approach

[Placeholder] I built a single metric governance layer in Snowflake with dbt, documented every definition, and aligned all departments on a shared semantic model. Board-level dashboards were built in Tableau using only the governed layer as a source.

Outcome

[Placeholder] Leadership reviews are now based on one version of the truth. The time spent reconciling reports dropped to zero. Investors have direct access to consistent, auditable metrics.

Growth analytics project
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04
BigQuery Python Metabase GA4

Improving Marketing Performance Through Attribution and Automation

deinhandy  ·  Attribution modeling, marketing analytics, automation, data pipeline fundamentals

Challenge

Performance marketing required improved attribution accuracy and more reliable reporting. Parts of the analytics workflow were manual, limiting transparency and optimization potential.

Approach

I developed a custom attribution model connecting marketing spend to sales outcomes and integrated offline conversions into Google Ads to improve optimization. Reporting and monitoring processes were automated using Python, and dashboards were built to support marketing and business teams. During the company’s acquisition, I supported the transition to new BI infrastructure.

Outcome

The improved attribution model reduced Cost per Sale by approximately 10% and increased visibility into campaign performance. Reporting became more automated and structured, strengthening data-driven decision-making across marketing operations.

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