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.

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.

Offerings

Three starter packages, three expansion packages. Each starts from a clear situation and ends in a defined deliverable.

  • Starter packages

  • 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.

    More details
    Process: you give me read access to your Databricks environment. I analyse your platform and identify where costs can be optimised. You receive a written report with prioritised 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

    Fabric vs. Databricks Assessment

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

    More details
    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

    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.

    More details
    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

    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.

    More details
    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

    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.

    More details
    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

    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.

    More details
    On a fixed rhythm (for example one or two days per week) I work directly in your platform: developing new pipelines, optimising 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.

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.

Career

Freelance Data & Lakehouse Consultant

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.

More details
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 selling projects that create dependency, not capability. 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. That is not a side effect. It is the deliverable.

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.

More details
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. The part I especially care about: 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.
Python SQL dbt Databricks Unity Catalog Declarative Automation Bundles (DABs) Databricks Dashboarding Databricks Apps Azure OpenTofu Terraform CI/CD Data modeling Data quality & testing

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.

More details
I spent four years at Evonik in the digitalization department, embedded directly next to the production plants. Working day to day with chemists, physicists, biologists and other computer scientists was a privilege: different backgrounds, one goal, and an excellent collaboration across all of them. 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.
Python SQL Databricks Kubernetes Docker Podman Data modeling Machine Learning Computer Vision

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.

More details
B.Sc. and M.Sc. in Applied Computer Science at Ruhr University Bochum, specialising 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.
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

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