Platform Engineering for Modern Data Teams

You write Python and SQL. We glue it all together: CI/CD, support, training, orchestration, testing, observability, so you can build the right things with confidence and keep your stack stable.

Modern data teams need more than tools; they need a platform that just works and a data platform team that is easy to work with.

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Why Most Data Teams Struggle

Your data stack may be holding you back.

Many teams are stuck managing fragile pipelines, patchy tooling, and manual workflows. Without structure, even great tools become a source of constant friction.

Instead of building insights, your team is stuck maintaining duct-taped pipelines.

That’s where we come in.

We bring platform engineering practices such as CI/CD, infrastructure-as-code, observability, and automation to modern data teams.

More importantly, we bring a data platform team that wants to work with you, that understands the importance of answering questions, helping out, and proactively fixing production issues.

We Build & Run Platforms That Scale

We’re not just advisors.

We’re hands-on data engineers and platform builders.

Every day, we manage:

  • 25,000+ flow runs
  • 5,000+ data flows
  • 100TB+ of live, production data We’ve seen what works and what breaks at scale.

The Real Problem Isn’t the Tools, It’s the Lack of Platform Engineering *

* Even with powerful tools like Databricks, Snowflake, Fabric, or Airflow, most teams end up stuck in the same frustrating patterns: quick ad hoc fixes and “temporary” scripts that linger, manual operations with minimal monitoring, unreliable deployments, and mounting technical debt. The root of the problem? Tools alone don’t make a platform.

What teams really need is platform engineering – the structure that turns scattered tools into a reliable, scalable foundation.

Meet the Team

We believe automation, developer experience, and clean architecture are non-negotiables for modern analytics.

With 30+ years of combined experience in infrastructure, data platforms, and data engineering, we help teams move faster and more confidently.

Alessio Civitillo

As an experienced financial analyst and software engineer, Alessio connects data strategy with execution, helping our clients unlock the hidden connections in their data and deliver value to their stakeholders.

Karol Wolski

Karol builds secure, cloud-agnostic data platforms at scale. With deep DevOps expertise, he unifies data sources, automates infrastructure, and streamlines hybrid operations.

Mateusz Paździor

Mateusz designs modular, future-proof data infrastructures with strong observability and operational excellence, ensuring they stay reliable, scalable, and aligned with evolving business needs.

The ultimate data platform goal? Stuff just works!

Unfortunately, many data analytics departments don’t end up where they had hoped. They struggle to build reliable data pipelines, they don’t manage to automate, and because of that, they have problems onboarding users and excessive manual work.

How High-Performing Data Teams build

Top teams borrow from software engineering to make analytics infrastructure scale:

  1. 1

    Automated from the start

    CI/CD, testing, versioning, docs as code

  2. 2

    Developer-friendly

    Fast feedback, intuitive tools, reusable patterns

  3. 3

    Secure & governed by default

    No shared creds, no blind spots

  4. 4

    Production-grade

    Resilient pipelines and clear monitoring

  5. 5

    Built to scale

    Handle more users, more data, fewer fire drills

Why do clients come to us?

Analytics Departments and Teams typically suffer from one or more of these 11 challenges

Unscalable.
Lacking strong tech foundations to build a scalable and long term solution
Unclear reference architecture.
Struggling to define a target architecture and to address specific analytics pain points
Unclear security model.
Not being sure what security and access management is needed to fulfil company policies
Shaky production.
Lacking a proper split of dev and prod environments and thereby making production less stable
Increasing tech debt.
Lacking a robust code review process to avoid accumulation of tech debt
Inadequate networking configuration.
Networking designed with applications in mind, making it difficult to stay secure and fast when developing analytical capabilities
Manual operations.
Time intensive, error prone, tedious daily manual work
Slow data ingestions.
Experiencing difficulties ingesting data from many applications and APIs
Struggling with best practices.
Lacking guidance in what tools to choose and avoiding making expensive mistakes
Difficulty in retaining key talent.
Experiencing challenges in keeping team members motivated
Difficulty in onboarding talent.
Experiencing challenges when onboarding new analysts to the complex web of tools in use

How We Work With You

We meet you where you are and then help your team scale confidently.

  1. 1

    Audit & Review

    We assess your platform: pipelines, infrastructure, governance, environments, and observability.

  2. 2

    Architect & Build

    We design and implement reusable infra-as-code (Terraform/ARM/CDK) and update existing systems.

  3. 3

    Automate & Integrate

    We streamline your entire stack from CI/CD to testing and monitoring.

  4. 4

    Operate & Enable

    We support and train your team while providing ongoing services as you take ownership of the platform.

Works with Your Stack

Whatever your data tools or infrastructure, we’ve integrated, scaled, and optimized them. Here’s how we support your stack:

  • AWS / Azure / GCP / On-premise

    Secure, scalable, and reliable infrastructure, wherever your data lives

  • Snowflake / Microsoft Fabric / Databricks / Amazon Redshift / BigQuery

    Environment design, access control, branching, multi-environment CI, automated testing, and analytics integration

  • Airflow / Prefect

    Infra-as-code, CI/CD, observability

  • DLTHub / dbt

    Ingestion and transformation with automated testing

What You Get

We build for people who love code, not YAML, infra ops, or late-night rebuilds.

Here’s what working with us feels like:

  • Ready-to-use templates (dbt, Python, orchestration)
  • Clear project structure – no debates, just consistency
  • Alerts when things break, not chaos
  • Partners who speak your language (dbt, SQL, Python, Airflow, Fabric, etc.)

Real Results

Read our client success stories:

1-Minute Deployments in 30 Days: Rebuilding a Legacy Data Platform for Scale

See how we rebuilt a legacy data platform in 30 days, enabling 1-minute deployments, automated CI/CD, and scalable self-service. Our “as-code” strategy eliminated bottlenecks and empowered lean teams to move fast and build for the future.

How Our ‘As-Code’ Approach Enabled a Smooth Migration of 450 Flows in Less Than 40 Working Days

Discover how we seamlessly migrated 450+ workflows to Prefect 2 in under 40 days without downtime. Learn how our ‘as-code’ strategy, automation, and smart planning made a complex transition fast, smooth, and scalable.

You Choose How You Work With Us

Audit + Proposal

Fast-track engagement to assess your architecture, identify gaps, and design your ideal platform.

Platform-as-a-Partner

Ongoing monthly support to co-own your platform operations, automation, and delivery.

Project Lift

End-to-end delivery: dbt CI/CD, Fabric observability stack, Snowflake branching infra, and more.

Start With an Audit

Not Sure Yet? Start With Clarity

Is your platform slowing you down? Most data teams aren’t sure until it’s too late.

Find out in 3 minutes with our scalability checkup.

  • Get a personalized score across five core dimensions.
  • Identify hidden bottlenecks before they become critical.
  • No fluff, just real, practical insights.
Take the Assessment

FAQ

Curious to know more? Schedule a call and get all your questions answered!

Let’s talk
Do we need a partner if we already use Fabric / Databricks / Snowflake?

Yes. These tools are powerful, but they need structure, integration, and governance to deliver.

Will you replace our engineers?

No. We coach, document, and train so your team can take over fully.

What happens after onboarding?

After onboarding, you choose what’s next. We either hand everything over, with complete documentation and support as needed, or continue working with you through a support service and ad hoc development.

Our Blog

Selected Articles. Check our blog for more.

SAP Data Ingestion with Python: A Technical Breakdown of Using the SAP RFC Protocol

Streamline SAP data integration with Python by leveraging the RFC protocol. This interview with the lead engineer of a new SAP RFC Connector explores the challenges of large-scale data extraction and explains how a C++ integration improves stability, speed, and reliability for modern data workflows.

CI/CD for Data Workflows: Automating Prefect Deployments with GitHub Actions

The final part of the Data Platform Infrastructure on GCP series covers CI/CD for Prefect deployments using GitHub Actions and Docker. Automate flow builds, worker updates, and streamline orchestration across environments.

Scaling Secure Data Access: A Systematic RBAC Approach Using Entra ID

Establish scalable, secure access controls for your data platform with a systematic RBAC strategy built on Microsoft Entra ID. This article outlines a five-phase implementation—from user persona mapping to automated auditing—designed to balance flexibility, compliance, and operational efficiency.