Most tech companies don’t struggle with the amount of data, but with its availability and trustworthiness. Data sits in a dozen systems, every team counts revenue its own way, and the report that lands on the board is a spreadsheet stitched together by hand. At that point it stops being about another tool and starts being about a data platform: a foundation, maintained over years, that analytics, product and decisions all rest on.
In our experience, the hard part isn’t picking the technology but wiring it into a coherent whole and keeping it running as the data grows. We don’t have a tech-company data platform case study in our portfolio, and we won’t pretend we do. What we do have is data and Big Data engineers who have built pipelines, warehouses and quality layers elsewhere, and whom we plug into your project.
Why does a tech company need a data platform?
A data platform isn’t just another database. It’s the layer that collects data from many sources, organises it and serves it from one trustworthy place. Without it, every team answers the same question differently and analysts clean data instead of drawing conclusions. With it, you can report on a single version of the truth and gain a foundation for things previously out of reach: scoring, personalisation, predictive models and, further down the line, an AI layer.
How do you build data pipelines: batch ingest and streams?
At the heart of the platform are data pipelines, the processes that move data from the sources to where you can work with it. They split into batch and streaming, and the choice follows how fresh the data must be. We usually build both paths side by side:
- batch ingest for data that only needs refreshing hourly or daily,
- streaming for events that must reach the platform almost instantly,
- an orchestration layer (e.g. Airflow) that guards order, dependencies and reruns,
- error and retry handling, so one unavailable system doesn’t topple the whole flow.
A well-designed pipeline is idempotent and observable: you can safely rerun it, and when something breaks you can see where immediately.
Warehouse or lakehouse: what should a data platform use?
This question comes up on almost every project. A data warehouse (e.g. Snowflake, BigQuery) excels at structured data and SQL analytics; it’s predictable and simple. A lakehouse (e.g. Databricks) combines cheap storage for raw data with the ability to compute on it as you would in a warehouse, and copes better with semi-structured data and ML workloads. It’s rarely a binary choice, so we often recommend a warehouse as the serving layer for analytics, with a lakehouse or data lake for raw data and ML. What matters isn’t which logo wins, but that the data model, costs and the team’s skills fit the company’s scale, and we help you make that call deliberately.
What is analytics engineering and a metrics layer?
Between raw data and the dashboard sits a layer that often gets skipped: analytics engineering. This is where raw tables turn into data models with clear definitions, tests and documentation, usually with dbt, so business logic lives in one place instead of across dozens of queries scattered through BI tools. On top of that you build the metrics layer (semantic layer), a single place where “revenue”, “active user” or “churn” each have one definition, so two teams get the same number for the same question. For a tech company this is often a bigger shift than the warehouse itself.
How do you monitor data quality on the platform?
A data platform is worth as much as the trust in the numbers that flow from it. That’s why we treat data quality as a separate, measured layer. In practice we wire into the platform:
- data tests (completeness, uniqueness, value ranges, key integrity),
- data contracts between the product team and the platform, so a change in a source doesn’t break reports,
- freshness and volume monitoring that alerts when data is late or half of it suddenly disappears,
- data lineage, a dependency map showing what breaks if you change a given table.
As a result, the problem raises an alert before the report is even produced.
How do you manage access, compliance and platform costs?
The more the platform can do, the more governance matters, meaning who has access to what. Once personal data and GDPR come in, a layer for access control, masking sensitive fields and audit isn’t optional; it’s the price of entry.
A separate topic that tends to surprise people is cost. A cloud warehouse or lakehouse bills by usage, so a badly written query or a pipeline refreshed too often can run up a bill nobody planned for. That’s why we design the platform with FinOps in mind from the start, so it scales with the company rather than against its budget.
When should you add an AI layer to the platform?
An AI layer is a natural extension of a good data platform, but it only makes sense once the foundation is stable. A predictive model is only as good as the data it receives, so the order “clean data first, AI second” is not a formality. Our competence here is real: within the RITS Group, our AI/LLM/data teams work on AI, LLMs and data, and as a group we’ve delivered projects with GenAI, RAG and language models. That lets us design the platform for later AI workloads from the outset: a feature store, data vectorisation, pipelines for training and serving models. We’re talking about competence, not an off-the-shelf AI rollout in your company.
Which skills do we provide for a data platform project?
The core is simple: we provide the people who will build and maintain the data platform. We shape the team around the stage of the project, not “just in case”, and verify skills before the start. Most often we staff these roles:
- a data engineer building pipelines, ingest and source integrations,
- an analytics engineer owning the data models, dbt and the metrics layer,
- a data architect setting the direction: warehouse vs lakehouse, the data model, costs,
- a platform/DevOps (DataOps) specialist guarding infrastructure, CI/CD and reliability,
- optionally an ML/AI engineer once the model layer comes into play.
Such a team can plug into an existing data department or take ownership of a slice of the platform end to end.
How do we do it at RITS?
We do it as a nearshore partner from Poland that brings ready data engineers, not a presentation about data. We staff the project with data and Big Data engineers who have real experience in pipelines, warehouses and data quality, and base the AI layer on the competence of the RITS Group. In a close time zone we join your working rhythm without a long ramp-up.
We’re honest about the limits: without a ready tech data platform case study we’re selling competence and people, not someone else’s success story, and for a decision-maker that’s a more honest starting point than a retouched reference.
Where to start building a data platform?
Start from one business question that’s hard to answer today (e.g. “how many genuinely active users do we have, and what does it cost to keep them”). It defines which data to collect, how fresh it must be and how it’s defined, and therefore the architecture of the platform’s first version. Build narrow but end to end. The second step is deciding who builds it: your own department, team augmentation or a dedicated team. We laid out the differences between these models in our article on IT engagement models.
Building a data platform, or want to put yours in order? Let’s talk - we’ll match data engineers and an architecture to your data.