Your Databricks platform, planned, built and in operation.

I design your data platform, build it with your team and take it into production. When the project ends, your team runs the platform without me.

Free 30-minute call View offerings
Role
Data Engineer & Solution Architect · Databricks
Available
from Q1 2027
Location
Remote · on-site within 100 km of Mülheim an der Ruhr
Stack
Databricks · Azure · Terraform
Simon Schröder

About me

Until 2026 I ran a Databricks platform on Azure at Fiege Logistics that entire teams relied on every day: from the migration through Terraform and CI/CD to governance with Unity Catalog. Before that: a 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.

Since 2026 I have worked as a freelancer, based in Mülheim an der Ruhr. What I care about is that something runs at the end, not that a concept looks good.

How I work

  • I work inside your platform. For a cost review I need read access to your Databricks environment, not a presentation.
  • Your developers build with me from day one. At the end, your team runs the platform itself.
  • When something breaks in production, I dig for the cause myself. That is how I ran the platform at Fiege for two years.
View career

Offerings

Three small starter packages for one specific question. Three larger ones for concept, migration and ongoing development.

  • Starter packages

  • Platform already running

    A report with a quantified list of measures: savings, effort and risk per item, explained in person in a handover workshop.

    Process: you give me read access to your Databricks environment. I analyze your platform and identify where costs can be optimized. You receive a written report with prioritized measures: savings potential, implementation effort and risk per item. In the handover workshop we go through every item together, giving you a basis for every decision that follows.

  • Decision still open

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

    Process: in a workshop I capture your requirements, your existing data landscape and your team's skills. I then assess both platforms against defined criteria: cost model, feature coverage for your concrete use cases, integration effort, lock-in risk and product roadmap. You receive a written recommendation with a criteria matrix, reasoning and counterarguments, so the decision can be understood and defended internally.

  • Proposal on the table

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

    Process: you provide the proposal and, if you wish, architecture documents confidentially. I review architecture, scope and price against your goals: is the scope justified? Is anything essential missing? Is anything oversized? You receive a clear, written verdict within days, confidential and without disclosure to third parties.

  • Expansion packages

  • Once direction is set

    A concept document for your target architecture: workspace layout, Unity Catalog design, migration strategy, CI/CD and an implementation plan in phases.

    The deliverable is a solid concept document: target architecture, workspace layout, Unity Catalog design with catalogs, ingestion and migration strategy, CI/CD and deployment approach, plus a concrete implementation plan with phases and responsibilities.

  • When you migrate

    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.

    I support your team: setting up the target platform according to the concept, migrating the first pipelines together with your developers, establishing CI/CD, governance and monitoring. You build alongside me from day one. In the end, your team runs the platform independently and no longer needs me.

  • Ongoing

    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.

    On a fixed rhythm (for example one or two days per week) I work directly in your platform: developing new pipelines, optimizing existing jobs, automating deployment and tests, keeping cost and governance under control. Predictable capacity without a full-time hire, and every change is made together with your team, so the knowledge stays with you.
Free 30-minute call

Beyond that: tailored advisory on an hourly basis. Details and pricing on request.

Career

since 2026

Independent

I make Databricks platforms production-ready: review costs, plan and execute the migration, keep developing the platform until your team runs it.

I went freelance in 2026 after running a Databricks platform that entire teams depended on every day. I had seen the other side of the market: large providers and system integrators whose projects are often set up so the client keeps needing them afterwards. My answer is a staircase of low-risk entry points. A Cost Review before anything else. A Second Opinion before anyone signs a proposal. A Fabric vs. Databricks Assessment while the decision is still open. Only then the bigger work: migration blueprints, guided migrations, engineering on demand. Every project ends the same way: your team runs the platform without me. I plan the handover to your team as part of the work from the start.

2024-2026

Fiege Logistics

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

In 2024 I joined Fiege Logistics as a platform engineer for an Azure-based data platform used daily by multiple internal developer teams. When I joined, the environment consisted of a wide range of standalone Azure services. The challenge: increase security, create cost transparency, ensure data availability. The platform had to deliver value to the company without becoming the bottleneck. Together with my team, I carried out the migration onto the Databricks platform. I automated infrastructure with Terraform, set up CI/CD, secured the platform and planned its network concept. In Unity Catalog I drafted data models and secured the data. I introduced Declarative Automation Bundles (DABs) as the default way to deploy and manage resources. As much time went into enablement: I coached teams and developers until they could build and operate their own solutions without waiting for my team. In December 2025 I presented our approach to multi-workspace pipeline orchestration at a Databricks User Group.

Focus areas: Databricks · Unity Catalog · Terraform · Azure · Declarative Automation Bundles (DABs) · CI/CD · OpenTofu · Python · SQL · dbt · Databricks Dashboarding · Databricks Apps · Data modeling · Data quality & testing

2020-2024

Evonik

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

I spent four years at Evonik in the digitalization department, embedded directly next to the production plants. I worked day to day with chemists, physicists, biologists and other computer scientists. Together we turned plant data into decisions. As product manager for the computer vision product, I ran the CV offering across the organization: models deployed for industrial quality control and product consistency. I built data pipelines on-premises and in the cloud, automated the analysis and provision of analytical data, and worked with process data from both continuous plants and batch processes. I handled energy consumption data for certification processes and implemented energy data management on Azure with Databricks.

Focus areas: Databricks · Python · Computer Vision · Machine Learning · Kubernetes · SQL · Docker · Podman · Data modeling

2014-2020

Ruhr University Bochum

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

B.Sc. and M.Sc. in Applied Computer Science at Ruhr University Bochum, specializing in software engineering and machine learning with a focus on computer vision. The spectrum ranged from classical statistical methods to deep learning architectures, the foundation for later moving between methodological depth and industrial practice.

Focus areas: Software Engineering · Machine Learning · Computer Vision

Talks & community

January 2026

Databricks Days: Host & Organizer (FIEGE)

Industry-focused Databricks workshop for Manufacturing & Logistics, hosted at FIEGE in Münster. Databricks guided attendees through an end-to-end scenario: AI forecasting, self-service insights with Databricks One and Genie, AI agents for operational decision-making, and real-time IoT data with Lakeflow and Zerobus. I organized the event on the Fiege side and hosted it.

View post on LinkedIn

December 2025

Databricks User Group Speaker

A talk on how we orchestrated data pipelines across several Azure Databricks workspaces at Fiege.

View post on LinkedIn

Free 30-minute call

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

No obligation: in the call we clarify where you stand, what you want to achieve, and whether I am the right fit.

Prefer direct email? contact@schroedersimon.de

Optional: helps me prepare