Soheil Ebrahimi
Data Architecture & Analytics Consulting

Build a data platform that survives growth.

I help startups and scale-ups design reliable analytics systems, data models, governance standards, and decision infrastructure before technical debt slows the business down.

From warehouse architecture and dbt design systems to KPI governance, marketing measurement, and AI-ready foundations, I turn fragmented data environments into systems teams can trust.

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10+ years across analytics engineering, data architecture, real-time systems, product analytics, and cross-functional data leadership.

Sources → Warehouse → Decisions
Tools I design and build with
AirbytedltdbtSparkAirflowDagsterPrefectSnowflakeBigQueryApache IcebergPostgresMongoDBGoogle AnalyticsPostHogTableauLookerRetool

Your data stack may be working today. That does not mean it is ready for what comes next.

Growing teams often reach the same point.

Revenue, finance, product, and marketing report different numbers.
The warehouse becomes harder to change with every new model.
Business logic is repeated across dashboards, notebooks, and spreadsheets.
Data ownership is unclear.
Pipelines fail silently or require too much manual intervention.
Analytics requests pile up faster than the team can deliver.
AI initiatives begin before the underlying data is reliable.
Senior engineers spend too much time fixing preventable architecture problems.

These are not only technical issues. They create slower decisions, duplicated work, wasted infrastructure spend, and declining trust in data. I help companies fix the system behind those symptoms.

Data architecture is decision architecture.

A warehouse is not valuable because it stores data. It is valuable when teams can use it to answer important questions consistently, understand why a metric changed, and act with confidence.

My work sits between engineering, analytics, product, and business leadership. I design systems that are technically sound, practical to operate, and aligned with how the company actually makes decisions.

Services

Practical architecture for companies that are growing faster than their data systems.

Data Architecture Assessment

A focused review of your current platform, operating model, risks, and priorities.

  • —Current-state architecture map
  • —Risk and bottleneck analysis
  • —AI-readiness assessment
  • —Prioritized 90-day roadmap
View the assessment →

Analytics Platform Blueprint

A target-state design for your warehouse, transformation layer, semantic model, orchestration, and delivery workflow.

  • —Target architecture
  • —dbt project structure and conventions
  • —Data contracts and ownership model
  • —Migration plan
View the blueprint →

Data Design System

A reusable operating system for how data products are named, structured, tested, documented, and owned.

  • —Modeling principles and naming conventions
  • —Metric definitions
  • —Reusable dbt patterns
  • —Ownership and lifecycle rules
Explore data design systems →

Fractional Data Architect

Senior architecture and analytics leadership without a full-time hire.

  • —Architecture decisions
  • —Roadmap prioritization
  • —Team coaching
  • —Governance and operating model design
Discuss fractional support →
Outcomes

What changes after the work is done

Clear ownership of critical datasets and metrics
Fewer conflicting numbers across teams
Faster delivery of analytics use cases
Lower infrastructure and processing costs
More reliable pipelines and models
A warehouse structure new team members can understand
Better documentation and reuse
Reduced dependence on individual employees
A realistic path toward self-service analytics
Stronger foundations for machine learning and AI
Better alignment between technical work and business priorities

Engineering depth. Product thinking. Business context.

Many data consultants focus only on tools. I focus on the whole system:

My background combines analytics engineering, data architecture, product ownership, stakeholder management, operational analytics, real-time systems, and applied AI. That means I do not simply recommend a technically elegant architecture. I design one your team can realistically adopt and operate.

How data is produced
How it is modeled
How definitions are governed
How teams discover and trust it
How decisions are made from it
How the platform evolves as the company grows
Soheil Ebrahimi
Soheil Ebrahimi

Senior Data & Analytics Engineer
Berlin, Germany

Selected experience
  • Owned a complete B2B analytics data domain and designed GDPR-compliant data sharing across multiple brands.
  • Coordinated data integration across six backend engineering teams and heterogeneous source systems.
  • Designed analytics-ready and AI-ready BigQuery data models for reporting and downstream machine learning.
  • Introduced streaming and batch architectures for real-time ingestion and low-latency dashboards.
  • Defined data contracts, documentation standards, ownership, and quality expectations for shared datasets.
  • Standardized dbt and Airflow-based analytics engineering workflows.
  • Reduced data processing time and infrastructure costs through architecture improvements.
  • Worked directly with product, operations, marketing, engineering, and leadership stakeholders.
Working at Wolt

At the intersection of analytics, marketing, and business decisions

At Wolt, I work in a high-scale analytics environment where data is used to improve marketing effectiveness, guide investment decisions, define performance frameworks, and support growth.

The work requires more than technical execution. It involves prioritizing the right questions, shaping analytics roadmaps, defining meaningful KPIs, partnering with senior stakeholders, and ensuring analysis leads to action.

Wolt is referenced only as professional experience. Consulting work is independent and is not affiliated with or endorsed by Wolt.

Approach

A clear process from uncertainty to an actionable system

01

Understand the business

We begin with the decisions, workflows, constraints, and growth plans the platform must support.

02

Map the current system

I review sources, ingestion, orchestration, warehouse structure, models, metrics, BI, and ownership.

03

Identify real bottlenecks

Not every problem requires a migration. I separate structural issues from local ones and prioritize by impact.

04

Design the target state

A practical architecture that fits your company size, team maturity, budget, and expected growth.

05

Create the operating model

A strong technical design still fails without ownership, standards, and a workable delivery process.

06

Deliver a roadmap

A prioritized plan distinguishing immediate fixes, medium-term improvements, and long-term investments.

I work best with companies that have momentum but are beginning to feel data complexity.

Seed to Series B startupsScale-ups (30-300 employees)SaaS companiesMarketplacesMobility and logisticsFintech and healthtechProduct-led companiesTeams hiring their first data engineer
You may be a good fit when
  • ✓Your company is growing quickly.
  • ✓Your data team is small relative to demand.
  • ✓Metrics are inconsistent.
  • ✓The warehouse has become difficult to maintain.
  • ✓You are about to hire senior data talent.
  • ✓You are evaluating a major migration.
  • ✓You want an independent view before committing to expensive implementation work.
Transparent starting points

Every engagement is scoped around your company, platform, and objectives.

Architecture Assessment

From €1,500

Book a call →
Platform Blueprint

From €3,500

Discuss →
Data Design System

From €4,000

Get started →
Fractional Architect

From €2,500/mo

Discuss →

The first conversation is used to determine whether the problem is suitable for a fixed-scope engagement or ongoing support.

Let's make your data platform easier to trust, operate, and scale.

Book an architecture call