Freelance Data & Lakehouse Consultant · Databricks

Your Databricks platform,
planned, built and in operation

I design your data platform, build it with your team and take it all the way to production. After that, your developers run everything themselves. No lock-in.

Free 30-minute call Offerings
Simon Schröder

About me

After years in data engineering I am convinced: platforms are not made good by their tools alone, but by the mindset of their makers and users. Teams that are merely served become dependent. Teams that are enabled create value and multiply it. That is why I work with Databricks: for many teams it is the right choice, combining innovation, broad feature coverage and maturity with an accessibility that makes it easy for the whole team to get started.

Computer science degree from Ruhr University Bochum, then Evonik, where I applied computer vision to real industrial use cases and worked with highly diverse data landscapes. Until 2026 I ran a Databricks platform on Azure at Fiege Logistics that entire teams relied on every day.

My roots are in the Ruhr Area: pragmatic, honest, direct, with the expectation that things must actually work in the end. What drives me in private and at work is the same: understanding how things connect and making them better.

Since 2026 I have worked as a freelance Data & Lakehouse Consultant. I design Databricks architectures, help automate platforms, build pipelines that hold up in production, and work in a way that leaves my clients independent once a project ends.

View career Offerings

Offerings

Three low-risk ways in, three offerings to build on. Each starts from a clear situation and ends in a defined deliverable.

  • Platform already running

    Databricks Cost Review

    A report with a quantified list of measures: savings, effort and risk per item, explained in person in a handover workshop. Your basis for every decision that follows.

  • Decision still open

    Fabric vs. Databricks Assessment

    A structured assessment of both platforms: cost, features, lock-in, roadmap. You get an explicit, written recommendation, within days.

  • Proposal on the table

    Databricks Second Opinion

    A review of your large-provider or system-integrator proposal: architecture, scope, price. You get a clear verdict, within days, before you sign.

  • Once direction is set

    Databricks Migration Blueprint

    Planning and design of your individually tailored target architecture: workspace layout, Unity Catalog concept and much more, exactly according to your needs.

  • When you migrate

    Databricks Guided Migration

    Together with your team, I set up your new platform. Afterwards, your team knows every detail and can move into operations without a transition gap.

  • Ongoing

    Databricks Engineering on Demand

    Engineering capacity for your Databricks platform: build and optimize pipelines, automate jobs and deployment, keep cost and governance under control. On a fixed rhythm, without a full-time hire.

Beyond that: tailored advisory on an hourly basis. Packages with a defined deliverable. Details and pricing on request.

How I work

Depth over surface: analysis from first principles, not templated slides.

Enablement over dependency: your team runs the platform from the day I leave.

Practice over slides: I build alongside you and solve problems in operations.

Availability

Free capacity from Q4 2026.

Available remotely worldwide – on-site within 100 km of Mülheim an der Ruhr.

Free 30-minute call

Career

Freelance Data & Lakehouse Consultant

Current
Independent

since 2026

I make Databricks production-ready for my customers: review costs upfront, plan and execute migration, keep developing the platform. A defined outcome, no lock-in.

Platform Data Engineer

Fiege Logistics

2024–2026

Built and operated a Databricks platform on Azure for multiple internal developer teams. Platform innovation, infrastructure automation, securing data.

Databricks Azure Infrastructure Automation CI/CD

Data Scientist & Data Engineer

Evonik

2020–2024

Deployed computer vision models for industrial quality control. Data pipelines (on-premises and cloud), energy data management on Azure with Databricks.

Computer Vision Data Engineering On-Premises Cloud Azure Databricks

B.Sc. & M.Sc. Applied Computer Sciences

Ruhr University Bochum

2014–2020

Specialized in software engineering and machine learning with an emphasis on computer vision. From classical statistical methods to deep learning architectures.

Talks & community

December 2025

Databricks User Group Speaker

Presented on orchestration of data pipelines on Azure Databricks within a multi-workspace data platform for large-scale logistics. Shared best practices with the broader community.

View post on LinkedIn

Free 30-minute call

Briefly describe your situation. You will hear back within 24 hours.

Optional: helps me prepare

Prefer direct email? contact@schroedersimon.de