About Christian
Work directly with Christian Stade-Schuldt, a Berlin-based data consultant and software engineer who connects strategy with implementation.
I am Christian Stade-Schuldt, a Berlin-based data consultant and software engineer. I help growing organizations turn fragmented data, manual reporting, and fragile workflows into reliable analytics, scalable data systems, and maintainable software.
From business question to working solution
Data initiatives often fail at the gaps between business needs, data foundations, and implementation. I work across those boundaries, connecting the decision a team needs to make with the pipelines, models, architecture, or software required to support it.
Clients work directly with me throughout the engagement. The person helping to clarify the problem is also involved in designing and delivering the solution, so recommendations stay grounded in what can be built, adopted, and maintained.
How I can help
- Business intelligence: Replace spreadsheet-heavy reporting and conflicting metrics with consistent, decision-focused reporting.
- Data engineering and architecture: Build dependable pipelines and data foundations that reduce manual repair, duplication, and platform risk.
- Data science and forecasting: Evaluate analytical use cases, test models against clear baselines, and make assumptions and uncertainty visible.
- Software engineering: Design, deliver, or stabilize data-intensive applications, APIs, and internal tools.
My technical work includes Python, Java, Django, SQL, Apache Spark, PySpark, AWS Redshift, and Snowflake. I have developed software, built data pipelines, created machine-learning models, contributed to open-source projects (see my code on GitHub), and led engineering teams. You can also review my professional background on LinkedIn and Stack Overflow.
Representative client engagements
Automated BI and reporting platform modernization
- Context: A fast-growing B2B organization relied on multi-tab spreadsheets and manual CSV exports to track pipeline health, customer retention, and revenue forecasts. Department leads frequently disputed numbers during executive meetings.
- Contribution: Conducted a reporting audit, aligned canonical metric definitions across finance and operations, and built an automated data warehouse pipeline feeding focused, role-based dashboards.
- Deliverables: Automated transformation models in SQL/Python, documented metric dictionary, role-specific KPI dashboards, and team training sessions.
- Outcome: Eliminated 15+ hours per week of recurring manual report assembly, reduced metric discrepancies to zero across leadership meetings, and enabled self-service exploration with hourly data freshness.
Resilient data pipeline and analytics infrastructure
- Context: An analytics team faced frequent pipeline failures, silent data corruption, and high cloud compute costs due to unmonitored batch ETL scripts running against operational databases.
- Contribution: Re-architected ingestion and transformation workflows using partitioned, tested data pipelines with automated schema validation and proactive alerting.
- Deliverables: Modular data pipeline codebase, staging-to-production deployment workflow, automated data quality test suite, and runbook documentation.
- Outcome: Reduced ETL execution runtimes by over 60%, eliminated pipeline failure outages during business hours, and provided the internal engineering team with clear observability and ownership.
A practical delivery approach
Every engagement begins with the business decision, workflow, or system creating the bottleneck. Together, we assess the available data and constraints, define a practical scope, and agree on meaningful measures of progress. Delivery happens in usable increments, with documentation and knowledge transfer considered part of the work.
This approach is a strong fit when:
- recurring reporting or unreliable data is slowing decisions;
- an internal team needs experienced implementation support; or
- a data initiative needs a credible path from idea to a maintainable production system.
What is slow, unreliable, or unclear today?
Share the decision, workflow, or system holding your organization back. I will respond directly so we can determine whether there is a practical fit and identify a sensible next step.