Recorded client call
“I find him very nice to work with and he actually delivers a very good quality. So I am really happy with him.”
Noa van der Veen · Strategy and operations, CheckyPro
Trusted data systems
Data Engineering
Pipelines, warehouses, dbt models, and data quality controls that make analytics and AI features depend on the same trustworthy source.
Partnerships and reviews
Certified partnerships
Rated by clients and team
- Clutch
What you get
Data infrastructure built for evidence
Every number should be traceable to a source, definition, transformation, and owner. We build that chain into the platform.
Batch + ELT pipelines
Idempotent ingestion from SaaS APIs, databases, files, and event stores with safe backfills and explicit retry semantics.
Warehouses + lakehouses
Snowflake, BigQuery, Postgres, and open table formats selected from volume, concurrency, governance, and cost requirements.
dbt transformation layers
Versioned staging, intermediate, and mart models with tests, lineage, review, and generated documentation.
Streaming + change data capture
Kafka, Kinesis, and CDC when second-level freshness is a product need: not because streaming sounds sophisticated.
Orchestration + scheduling
Dagster, Airflow, or Prefect flows with dependency-aware retries, SLAs, backfills, and useful failure alerts.
Metrics + semantic layers
Governed definitions for revenue, usage, activation, and risk so product, finance, and operations use the same number.
Data for AI products
Training datasets, feature pipelines, vector ingestion, evaluation datasets, and lineage from source to model input.
Quality, privacy + lineage
Freshness, volume, schema, access, and anomaly controls that catch bad data before it reaches a dashboard or model.
Delivery
How we build reliable data platforms
- 01
Inventory sources + consumers
We map where data originates, who uses it, the decisions it supports, and where definitions currently disagree.
- 02
Define contracts + model
Source contracts, ownership, warehouse layers, metric definitions, privacy boundaries, and recovery targets are made explicit.
- 03
Build tested pipelines
Ingestion, transformation, orchestration, CI, and backfills ship together: with observable runs from the first production load.
- 04
Prove + hand over
We reconcile outputs against source systems, document lineage and runbooks, then train your team to extend the platform safely.
Every pipeline tested, every number traceable to source
Fit
When a data engineering engagement fits
Best for
- Dashboards or AI features disagree because source data is inconsistent
- Manual exports and scripts have become operational dependencies
- You need reliable training or evaluation datasets for an AI product
- Pipeline failures are found by users instead of monitoring
Not for
- A single spreadsheet cleanup with no recurring data flow
- Real-time streaming without a user or operational need for second-level freshness
- A new warehouse requested before source ownership and metric definitions are resolved
Outcome
Data that product and leadership both trust
When pipelines have contracts, models have tests, and metrics have owners, you stop arguing about which number is right. You start using the data to make decisions; and the dashboards, AI features, and reports all agree.
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Related services
Put reliable data to work
AI Development
Use governed data for retrieval, fine-tuning, evaluation, and production AI features.
Explore serviceBackend Development
Build the operational systems that produce and consume product data.
Explore serviceDevOps & Cloud
Run pipelines and warehouses on secure, observable, cost-controlled infrastructure.
Explore serviceTestimonials
What founders say after the build
Verbatim quotes from teams we shipped to production. We publish testimonials with written sign-off on file.
4.6/5across 58 verified client reviews.Read them all at source
Data engineering questions
We favour the simplest data architecture that can meet your reliability, governance, and freshness requirements.
A well-modelled Postgres is enough for many teams. We recommend Snowflake or BigQuery when scan volume, concurrency, workload isolation, governance, or elastic compute justifies the extra platform. We do not introduce a warehouse to decorate the architecture diagram.
Can your team trust the data?
Bring us the sources, the reports that disagree, and the pipeline failures. We will map what is broken and propose the smallest system that makes the data dependable.