Every growing company eventually meets the same resistance.
- Dashboards do not match
- Pipelines break more frequently
- Stakeholders begin to question the numbers
What worked with three engineers is not successful with ten, and what was once fine at ten engineers will lead to significant cracks when your team reaches twenty engineers.
When you reach that moment, managing your data infrastructure will become less about the tools needed to do the job and more about the overall structure of the organization.
As a VP or Head of Data responsible for scaling data infrastructure management, your challenge is not simply to scale pipelines, but to scale the operating model behind them.
100 CTOs. Real Expectations
This report shows what actually predicts delivery success and what CTOs discover too late.
The Failure Mode That Is Common
Early stage teams do create pipelines using very little documentation or defined ownership focusing more on rapid pace than structure.
This works fine when you have:
- Low number of data
- Few stakeholders involved
- Limited amount of use cases
As the company grows, however, this style will break down.
What Will Occur at Scale
When going from 3 engineers to 15:
- The creation of the number of pipelines increases exponentially
- There is uncertainty of who owns the pipelines
- There will be an increase in the failures
When there are 30 engineers, you will be able to see:
- Teams have done duplicate work
- There are inconsistent metrics between the teams
- Features built will be hard to troubleshoot due to the duplicate metrics
The Challenges of Managing Data Infrastructure in 2026
Technology has become increasingly more complex:
- Real-time pipelines are now commonplace
- Workloads based on AI/ML demand clear and standardised data
- There are more data-generating systems than at any time in the past
- Regulatory requirements will inhibit your ability to grow your business in both a linear and exponential manner
The Characteristics of a Successful VP or Head of Data
The successful Head of Data or VP of Data will possess:
- Clear ownership of all data pipelines across teams
- Ensure that there are reliable definitions of all data across multiple teams
- Use architectures that can grow without too much reconstruction
- Have bottlenecks that reduce the entire dependency
A Real-Life Example You Can Relate To
A product team has developed a new feature that relies on its performance to provide analytics, and to accomplish this, the product team builds another pipeline that has different metrics and saves the data in a different part of the business instead of utilising the existing data pipeline.
After six months, you will have three versions of the same metric across different teams.
This was not due to a lack of tools and technologies; it was due to a poorly structured team structure.
Pre-Conditions
Different Data Teams & Pipelines
All data teams and pipelines should have:
- A defined owner or responsible party
- Clear responsibilities or duties for each team member
- Documented Service Level Agreements (SLAs)
A model that describes what will happen within each team and pipeline should be developed:
- The infrastructure and tooling belongs to the platform team
- The data product and business logic belong to the domain teams
Establish Baseline Tooling
You do not need to have the perfect set of tools established but you do need to have some consistency set up.
The teams need to have access to:
- A centralized data platform (this could be a data warehouse or a data lakehouse)
- Pipeline orchestration tools for managing how their data flows through the data pipeline
- Transformation code stored in a version control system
- Monitoring and alerting for when the pipelines fail
Without baseline tooling in place, each increasing data team will create a chaotic environment.
Establish Data Contracts
As organizations scale and grow the chances of experiencing schema drift become very probable.
Data contracts will ensure:
- The producers of data will be establishing and defining the schema
- Consumers of data will rely on the producer's schema and established interfaces
- Any changes made to the producer's schema will be communicated to the consumer(s) as early as possible
Align Stakeholders Early
When scaling the data teams, several areas will be affected:
- Engineering
- Product
- Analytics
- Business teams
There should be agreement between parties in the following areas:
- Clear data definition
- Priority
- Tradeoffs
Secure Budget and Hiring Plan
In order to scale you need to:
- Hire engineers who have the right capabilities
- Invest in infrastructure
- Allocate time for refining documentation and establishing governance
Define Success Metrics
Before you begin scaling you need to have clarified success metrics:
- Reliability of the Pipeline
- The Freshness of Data
- The Trust of Stakeholders
- The Productivity of the Team
This will help ensure everyone is on the same page as the team grows.
Section Three: Phase One: Assess Current State
Before beginning to scale you'll first want to get a better understanding of your existing state.
1. Audit Current Team Structure
To get a better feel for what's going on you'll want to map out:
- Who's owning which pipeline
- What teams are consuming which data
- Where there's overlap on responsibility
Mapping out your structure will help you to find no-gaps, or sheer incapacity.
2. Inventory Your Data Stack
Create an inventory of the following items:
- Data Sources
- Data Pipelines
- Data Storage Systems
- Business Intelligence Tools
For each source of data you'll want to identify and clearly define:
- Owner
- Refresh Rate
- Known Issues
3. Identify Bottlenecks
Commonly teams will experience at least 3 major bottlenecks:
a) Data Ownership Gaps
- Critical Pipelines with lack of data ownership
b) Dependency Bottlenecks
When teams are waiting on each other, as well as adding to the workload of centralised teams, there can be a build up of work that your entire team must do.
c) Lack of Standardization
Different teams are using different tools and have different ways of doing things within those tools.
This will have an impact on the quality of your data.
Map Data Flows
You can see this very easily by creating a simple diagram to show the flow of data through your organization.
This gives you a view into:
- Redundant pipelines
- Hidden dependencies
- Inefficiencies
Evaluate your SLAs and How Reliable are they?
You should also be able to see:
- How often your pipelines fail
- How long it takes you to repair them
- How often your stakeholders need your data
Prioritize Improvements
You should then be able to split your opportunities for improvement into a number of categories such as:
Quick Wins:
- Assign a person to be responsible
- Fix any critical pipeline issues
- Standardise any of the key metrics within your organisation
Long Term Initiatives:
- Change the way your teams are structured
- Create a data contract between your platform and domain teams
- Create more visibility in the data being produced
Output
You should now have:
- A better understanding of how your organisation is currently structured
- Clear priorities for improvement
Section 4 - Phase 2 - Designing your Target Architecture
If you want to scale as an organisation you will need to take a deliberate approach to design your target architecture.
1. Define Guiding Principles
The design principles for your organisation should include:
- Ownership should be clear
- A modular architecture
- An observability first design
- Contract driven pipelines
2. Select your Team Models
There are three common types of team models.
Centralised Model
In this model you only have one team that is responsible for everything.
The main advantage of this model is:
- A high level of consistency
However this is a bottleneck when you scale out the size of your organisation.
Federated Model
This model is ideal if you want to have a lot of independence within your teams.
Therefore each team is responsible for all aspects of the data used by their respective domains.
The downside of this model is:
- You may not have a lot of consistency between teams
Hybrid Model (recommended)
In this model, you have a combination of both a central platform team and separate domain teams.
With this model, you can find a good balance of:
- Control
- Flexibility
3. Define your Responsibilities Clearly
Platform Team Responsibilities
- Provide Infrastructure
- Provide Tooling
- Provide Governance
Domain Team Responsibilities
- Provide Data Products
- Provide Business Logic
- Provide Use Case Pipelines
4. Design for Observability
Designing your architecture for observability will include:
- Monitoring the pipelines
- Checking the quality of the data being produced
- Tracking the lineage of the data being produced
Standardisation of Tools & Practices
Standardisation of tooling and practices helps to minimise fragmentation.
Standardising effective tooling and construction of shared templates minimises fragmentation, supports enforcement of best practices through the use of a unified tooling and practice platform therefore enables those involved with the project at any point during development to continue to conform with the same level of accuracy.
Documentation of Assumptions
Scaling decisions are based upon the following criteria:
- Expected amount of data growth
- Team size
- Types of use cases
The scaling of your plan will be influenced by an ongoing assessment of these criteria.
Phase 3 - Build, Test, Rollout Incrementally
Gradual scaling will be the most effective way to add capabilities without causing disruption.
Select one domain for your pilot project
Your pilot project could include:
- Product analytics
- Marketing data
Once you have established and validated your model in that domain, you will be able to establish additional domains as required.
Validation of your model will determine:
- The effectiveness of ownership
- Reduction in dependency
- Increased reliability of your data
Parallel Systems Run
Keep current pipeline systems operational until your proposed changes are made to allow you to compare the outputs of both pipeline systems.
Test Automation
Automation of tests such as:
- Schema validation
- Data quality verification
- Pipeline testing
Will increase the reliability of your processes.
Instrument Everything
Instrumenting your entire pipeline is a requirement to determine the following:
- Pipeline latency
- Error rates
- Data freshness
Gradual Scaling
Once proven to work, gradually expand your use of your new tools and processes to all applicable domains.
Key Insight
Growth is determined not by speed but by effective growth that is predictable and controlled.
Phase 6 - Measuring Success & Iterating
Once established, it becomes critical to measure performance against agreed upon performance metrics.
Define SLOs
Establish the following key SLO metrics to measure your pipelines:
- Pipeline Availability
- Throughput of data
- Accuracy of data
For example:
- 99.9% available
- < 10 Minutes Latency
- < 1% Discrepancy
Build Stakeholder Dashboards
By building dashboards for each stakeholder, you will allow them to track the overall status of your architecture as well as provide metrics regarding the condition of their respective pipelines.
Track Team Productivity
Tracking team productivity will allow you to see:
- How long it takes your team to build the required pipelines
- How long it has taken to resolve each incident
- The number of incidents caused by dependencies
Conduct Regular Retrospectives
Conduct weekly retrospectives during the first three months post transition for the new processes to identify any deviance from expected performance levels and ensure they are adjusted prior to being addressed in future cycles.
Monitor Leading Indicators
Monitoring leading indicators such as:
- Frequency of incidents
- Adherence to your SLA
- Data usage
Will identify long-term success for your team.
60% Overhead Reduction Guide
Inside a one-quarter overhead audit that pulled a five-person data team back from 67% firefighting.
Call to Action
Logiciel POV
The success of scaling data infrastructure is based on the fundamental principle of building a design capable of sustaining growth.
The most successful teams create a model for data infrastructure management based on the following principles:
- Architecture
- Ownership
- Processes
Logiciel Solutions can partner with an organisation to support the effective scaling of both your data engineering team and platforms while helping you ensure reliability, consistency and sustainability of growth.
If your data systems are struggling to keep up with your current growth, complete a redesign of your structure.
Explore how Logiciel’s AI-first engineering teams can assist you in scaling your data infrastructure and engineering teams without disrupting what is already working.
Frequently Asked Questions
How should my data engineering team be structured?
Most high performing teams have adopted a hybrid model with a central platform team and individual domain specific teams. This blended approach allows for both a consistent methodology and allows for scalability without creating bottlenecks and has proven to be effective.
When is the right time to transition from a centralised to a federated team?
Usually at the point where the size of your team exceeds approximately 10-15 engineers and there are indications of dependencies slowing down the delivery of your projects. At this point domain ownership can be employed while retaining a central platform team.
What is the most challenging aspect of scaling a data engineering team?
The most challenging aspect of scaling a data engineering team will not be an issue of technology. However, it will be more of a challenge of the organisation. The lack of ownership, the confusion around roles/responsibilities and the lack of consistency in how operations are executed will create many more issues than the limitations of tooling.
How long does it take to be able to successfully scale your data engineering team?
Typically, effective restructuring of your data engineering team will take 8-12 weeks. However, it is common for an effective organisation restructuring to be an iterative process which extends over several months.
