Data pipeline engineering
Batch and streaming ingestion from operational systems, files, and events with quality checks at the door.
Overview
We engineer data platforms that power enterprise intelligence — from ingestion pipelines and data lakes to real-time analytics and executive dashboards. Our solutions are built for scale, governance, and compliance.
Related capabilities live on our capabilities page. To scope an engagement, book a consultation.
Key Benefits
- Modern data lake architecture
- Real-time streaming pipelines
- Business intelligence platforms
- Data governance and quality
What We Deliver
Data platforms that leaders can trust
Enterprise data engineering and analytics — pipelines, lakes, warehouses, real-time streams, and BI — with governance so numbers match across finance, operations, and AI.
Lakehouse & warehouse
Modern lake and warehouse design (Snowflake, BigQuery, Redshift, Databricks, Fabric) with clear domains and ownership.
Real-time analytics
Streaming architectures for operations dashboards, fraud, and supply-chain exceptions.
Business intelligence
Semantic models and executive dashboards that do not fork into twelve versions of revenue.
Data governance
Catalogs, lineage, access policies, and quality SLAs that satisfy security and audit.
AI-ready features
Feature stores and governed training sets so machine learning is not built on spreadsheet extracts.
Industry Use Cases
Analytics where a wrong number is expensive
We build for decisions — clinical, financial, operational — not vanity dashboards.
Delivery Model
Domain data products, not a single mega-warehouse
We publish data products with owners and SLAs. That is how analytics and AI stay current after the first dashboard ships.
- 01
Source & use-case map
Which decisions, which systems, and which quality bar each domain must meet.
- 02
Platform & model
Lake/warehouse choice, identity, and a canonical model for the first domains.
- 03
Pipelines & products
Ingest, test, and publish the first data products with lineage.
- 04
Consume & govern
BI, APIs, and access reviews so the platform becomes the default source of truth.
Platforms & Stack
Modern data stack, enterprise controls
We implement the warehouse or lakehouse your cloud and skills already support, then add the governance layer most stacks skip.
See the broader engineering bench on our capabilities page, or talk through a consultation.
- Snowflake
- Databricks
- BigQuery
- Redshift
- Microsoft Fabric
- dbt
- Airflow / Dagster
- Kafka
- Power BI
- Looker
- Tableau
- Unity / Purview
FAQ
Frequently asked questions about data engineering
For CDOs and analytics leaders replacing a patchwork of extracts.
Do you build data lakes, warehouses, or both?
We implement the pattern your use cases need — warehouse, lakehouse, or a combination — on Snowflake, Databricks, BigQuery, Redshift, or Fabric.
How do you keep numbers consistent across teams?
Domain data products, a semantic layer, and named owners. If two dashboards disagree, we treat that as a platform defect.
Can the platform support AI and BI at the same time?
Yes. Governed features and training sets sit on the same quality bar as executive reporting.
How do you handle data governance and access?
Catalog, lineage, row/column policies, and reviews that match your security and privacy requirements.
What does a first increment look like?
One or two domains in production with tested pipelines and a dashboard or API consumers already use — usually in 8–12 weeks if sources are reachable.
Ready to Build What's Next?
Partner with OVN Technologies to engineer intelligent systems that drive measurable business impact.
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