Data Engineering
Data platforms people actually trust
Pipelines, warehouses, and real-time systems that turn scattered operational data into a dependable foundation for reporting, products, and AI.
Overview
When two dashboards disagree, people stop trusting both. Most data problems are engineering problems: fragile pipelines, unclear ownership, and no testing.
We build data platforms with the same rigor as production software. Version control, automated tests, lineage, and service levels for the numbers that matter most.
Capabilities
What Data Engineering covers
Data Engineering
Tested, observable pipelines built as code and owned by clear domains.
Data Platforms
Lakehouse and warehouse platforms sized for today and ready for growth.
Data Warehousing
Dimensional models that make reporting fast and consistent.
ETL Pipelines
Batch and incremental loads from ERP, CRM, and SaaS sources.
Business Intelligence
Governed semantic layers and dashboards that answer real questions.
Data Analytics
Analysis and modelling that connect operational data to business outcomes.
Real-Time Data Systems
Streaming architectures for events that cannot wait for a nightly batch.
Database Architecture
Schema design, performance tuning, and replication for demanding workloads.
Data Migration
Validated migrations with reconciliation reports and zero data loss.
Technical challenges
Examples this service may address
Project requirements vary. These examples can help frame an initial discussion.
Numbers that do not match
Finance and operations presenting different figures for the same metric.
Reports that take days
Analysts stitching spreadsheets together every month end.
Pipelines that break silently
Stale data discovered only after decisions were made on it.
Not ready for AI
Useful data locked in systems that models and products cannot reach.
How we deliver
How the work runs
Sequence and architecture are set by your goals and constraints.
- 1
Audit
Trace critical metrics back to their sources and document where trust breaks down.
- 2
Model
Design a data model and ownership structure aligned with how the business works.
- 3
Engineer
Build pipelines with tests, lineage, and alerting from the first release.
- 4
Enable
Train analysts and product teams to use and extend the platform confidently.
- ERP
- CRM
- Product events
- Files
- Batch ETL
- Streaming
- Change data capture
- Lakehouse
- Warehouse
- Semantic layer
- Dashboards
- ML features
- Data APIs
Technology stack
Examples of technologies that may fit this service area.
- Snowflake
- BigQuery
- Databricks
- dbt
- Apache Airflow
- Kafka
- Spark
- PostgreSQL
- MongoDB
- Redis
- Power BI
- Looker
FAQ
Common questions
It depends on your data volume, variety, and team skills. We often start with a warehouse for reporting and add lakehouse capabilities when machine learning or unstructured data becomes important.
Yes. We connect to Power BI, Tableau, Looker, and others, and focus on making the underlying data consistent.
Automated tests on every pipeline, freshness and volume monitoring, data contracts with source teams, and clear ownership for each dataset.
Data controls should be scoped against the applicable requirements with your security and legal stakeholders.
Prefer to talk it through?
A private consultation, 30 minutes with the founder, in your time zone.
Planning a data engineering initiative? Let us talk.
Tell us what you are working on. We start by understanding the problem, the people, and the systems involved.