Platform Engineering for Reliable Data Teams

Your data team should be building new capabilities, not managing unstable deployments, recurring incidents, and blind spots in platform health.

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Your Data Platform May Be Slowing the Business Down

Most platform problems start when complexity outpaces structure.

Pipelines multiply. Ownership becomes unclear. Deployments become risky. Monitoring is incomplete. Engineers spend more time maintaining the platform than building new capabilities.

Eventually, the platform still works, but nobody fully trusts it.

AI initiatives move forward on unstable foundations, delivery slows despite increased engineering hiring, operational risk increases, and costs grow without clear visibility.

That’s where we come in.

We help modern data teams introduce the engineering structure their platforms are missing, making systems easier to deploy, monitor, scale, and operate over time.

We Build & Run Production-Grade Data Platforms

We are not strategy-only consultants.

We are hands-on platform architects and engineers operating real production systems every day.

Every day, we support:

  • 25,000+ flow runs
  • 5,000+ orchestrated data flows
  • 100TB+ of live, production data
  • Multi-environment deployments across cloud and hybrid infrastructure

The Real Problem Isn’t the Stack. It’s the Lack of Platform Engineering *

Most modern data teams already use strong tools: Databricks, Snowflake, Fabric, Airflow, dbt, Prefect, and more. But tools alone do not create a scalable platform.

Without strong engineering foundations, platforms become harder to deploy, monitor, and operate reliably.

That is why many teams struggle to scale AI and analytics initiatives even after major cloud investments.

What changes the trajectory is platform engineering: standardized deployments, infrastructure-as-code, testing, observability, orchestration, and clear operational ownership.

Meet the Team

We believe modern data platforms should be automated, observable, reproducible, and easy to operate.

With 30+ years of combined experience across platform engineering, infrastructure, analytics, and data engineering, we help teams regain control of systems that have become difficult to scale and maintain.

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 goal of a modern data platform? Predictable systems. Reliable delivery. Fewer operational risks.

Many data teams start with good tooling and strong engineers. Over time, complexity accumulates: deployments become fragile, incidents increase, and delivery slows down. High-performing teams operate differently. Their platforms are designed to scale safely, surface risks early, and support growth without operational instability. The goal is a platform your team can confidently build on.

How High-Performing Data Teams Operate

The best data infrastructures borrow from software and platform engineering to scale complexity without losing control.

  1. 1

    Standardized deployments

    CI/CD, infrastructure-as-code, automated releases, reproducible environments

  2. 2

    Built-in observability

    Monitoring, alerting, lineage, and early warning systems that surface issues before they escalate

  3. 3

    Reliable operational foundations

    Environment isolation, rollback strategies, automated recovery, and clear SLAs

  4. 4

    Developer-friendly workflows

    Reusable patterns, fast feedback loops, strong documentation, and less manual work

  5. 5

    Scalable architecture

    Platforms designed to support more users, more data, and more AI workloads without operational chaos

Why do clients come to us?

Most teams reach out when their platform becomes difficult to scale, operate, or trust.

Reactive operations
Incidents, broken pipelines, and operational work consume too much engineering time.
Slow delivery despite hiring
More engineers join the team, but delivery does not improve because maintenance dominates capacity.
Fragile deployments
Changes feel risky. CI/CD is inconsistent, and production stability depends on manual caution.
Limited visibility
Teams lack a clear, real-time view of platform health, bottlenecks, failures, and emerging risks.
Growing technical debt
Temporary fixes accumulate over time until every platform change becomes slower and harder.
Unclear architecture direction
The platform evolved organically, and there is no shared target architecture or operational model.
Weak environment separation
Development, staging, and production are not properly isolated, increasing operational risk.
Data warehouse sprawl
Hundreds of undocumented tables, unclear ownership, and inconsistent business logic reduce trust in data.
Manual operations
Deployments, recovery steps, and operational processes still depend heavily on manual work.
Rising infrastructure costs
Cloud spend grows faster than visibility into usage, optimization, or business value.
Knowledge concentration
Critical platform knowledge is concentrated in a few individuals, creating operational risk.
AI readiness concerns
Leadership wants measurable AI outcomes, but the platform is not stable enough to scale confidently.

How We Work With You

We focus on restoring predictability, reliability, and operational clarity, without forcing unnecessary platform rewrites.

  1. 1

    Assess & Identify

    We evaluate your architecture, orchestration, CI/CD, observability, and operational workflows to identify the highest-impact improvements.

  2. 2

    Validate & Prove

    We implement focused improvements in a controlled scope to validate scalability, reliability, automation, and operational impact before a larger rollout.

  3. 3

    Production Implementation

    We roll out production-grade infrastructure, CI/CD, observability, testing, orchestration, and operational improvements across the platform.

  4. 4

    Enable & Support

    We document, train, and support your team while helping establish long-term operational ownership and scalable engineering practices.

Works With Your Existing Stack

We do not force platform replacements. We help modernize and operationalize the infrastructure and tooling you already depend on.

  • AWS / Azure / GCP / Hybrid Infrastructure

Production-grade infrastructure design, autoscaling, environment management, networking, security, observability, and operational reliability.

  • Snowflake / Fabric / Databricks / BigQuery / Redshift

Environment strategy, CI/CD, access control, testing, deployment workflows, governance, and scalable operational foundations.

  • Airflow / Prefect

Reliable orchestration, infrastructure-as-code, observability, deployment automation, and resilient execution models.

  • dbt / DLTHub

Version-controlled transformation and ingestion workflows with automated testing, reproducibility, and scalable development practices.

What Working With Us Feels Like

We build for teams that want to spend more time delivering and less time managing platform friction.

  • Production-grade foundations instead of duct-taped workflows
  • CI/CD, observability, and deployment patterns that reduce operational risk
  • Reusable templates and standardized project structures
  • Clearer ownership and more predictable operations
  • Fewer incidents and less firefighting
  • Better visibility into platform health, failures, and bottlenecks

Real Results

Read our client success stories:

Unifying 10+ ERPs into One Reusable Data Platform

Learn how we helped a global manufacturer move from fragmented monthly reporting to daily visibility by transforming a fragmented ERP landscape into a centralized, reusable data platform. We enabled reliable daily insights at enterprise scale by replacing manual consolidation with automated ingestion, controlled orchestration, and traceable business logic.

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

Assessment + Roadmap

A focused engagement to evaluate your platform, identify operational risks and inefficiencies, and define a prioritized improvement roadmap.

Platform-as-a-Partner

Ongoing support across platform operations, observability, orchestration, automation, CI/CD, and long-term platform evolution.

Delivery Projects

Hands-on implementation of production-grade improvements across CI/CD, orchestration, observability, autoscaling infrastructure, deployment automation, and environment standardization.

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 still need platform engineering support if we already use Snowflake, Fabric, Databricks, or Airflow?

Yes.

Modern tooling solves part of the problem, but most operational issues come from how the platform is structured and operated. Deployments, observability, environment management, testing, orchestration, and operational ownership all need to work together predictably as the platform scales.

The tools matter. The engineering discipline around them matters even more.

Will you replace our internal engineers?

No.

Our goal is to help your team operate more predictably and spend less time firefighting.

We document, automate, standardize, and transfer knowledge so your engineers can focus on delivery instead of operational overhead.

Do you replace our existing stack?

Usually not.

Most engagements focus on improving reliability, scalability, deployment workflows, observability, and operational maturity within your current ecosystem.

The problem is rarely the existence of tools. It is how the platform behaves as complexity grows.

What happens after onboarding?

That depends on your team.

Some clients move into full implementation and long-term operational improvements. Others take ownership internally with our support where needed.

The engagement is designed to create a clear decision point, not force a long-term commitment.

What kinds of problems are you usually brought in to solve?

Usually, some combination of increasing incidents, fragile deployments, weak observability, technical debt, unreliable orchestration, rising cloud costs, inconsistent operational processes, and AI initiatives is struggling on top of unstable foundations.

Our Blog

Selected Articles. Check our blog for more.

Running dbt Rescue Rebuild in Production: Operational Playbooks, Failure Models, and Recovery Patterns

Go beyond the setup and into real-world execution. Learn how we run dbt rescue rebuilds in production: scoping dependencies, managing warehouse contention, handling incremental models, and recovering from outages with precision, without introducing new risks to pipeline stability.

The Rescue dbt_rerun Deployment: Rebuilding Changed and Broken Models Without Disrupting Production

Keeping production data correct after a dbt change is harder than it looks. Learn how we introduced a dedicated rescue deployment to rebuild exactly what’s needed and when it’s needed, bringing consistency back to production data without costly full reruns or pipeline disruptions.

Why Data Teams Struggle Without Separate Dev and Prod Environments

When development and production share the same data environment, even small changes can trigger costly outages. This article explains why separating dev and prod is foundational for reliable analytics, and how teams can do it without overengineering or blowing the budget.