+22%Conversion
1.2sLCP
Emerging Technology
Data Engineering Services
Pipelines and warehouses you can trust: tested, documented, observable.
quality tests in CI by default
Data pipeline diagram with source nodes, transform stages and sinks connected by flowing streamsProblems solved
What this removes.
scattered source data
every report starts with an export hunt
broken nightly jobs
meetings open with which number is right
no data contracts
upstream changes break downstream silently
warehouse sprawl
three tools, three answers, zero trust
no lineage
audits become archaeology projects
What's included
What you get.
- data architecture + domain map
- ELT pipelines (Airflow/Dagster)
- warehouse modeling (dbt)
- streaming backbone (Kafka)
- data quality tests (Great Expectations)
- lineage + docs (dbt docs)
- governance + access policy
- monitoring + alerting
- cost optimization report
- handover + enablement
Scope
Scope, clearly drawn.
Core scope is fixed. Add-on prices are indicative — not quotes.
Included in every engagement
- source audit + inventory
- model design (staging/marts)
- pipeline build + orchestration
- quality gates in CI
- lineage + docs publish
- handover + training
Optional add-ons
- streaming backbone (Kafka)+$8k
CDC via Debezium
- data catalog (DataHub)+$6k
lineage + discovery
- PII masking + governance pack+$5k
- lakehouse migration (Iceberg)+$9k
Our approach
How we run it.
Source audit
inventory + freshness SLAs
Model design
contracts before code
Pipeline build
idempotent + tested
Quality gates
alerts on drift + volume
Handover
docs + enablement sessions
Pricing signal
What it typically costs.
Typical range
$30k-$150k
Midpoint $90k · final quote after a fixed-scope discovery.
data quality tests in CI by default
Technologies
Tools we reach for.
Case evidence
Proof.
Our team's work, before DosVectis.
FAQ
Questions, answered.
Cloud-agnostic?
Yes - open stacks preferred; we run on AWS, GCP, Azure or on-prem.
Do you document models?
Yes - dbt docs + lineage published with every mart.
Streaming vs batch?
Use case decides; batch by default, Kafka where latency earns it.
Which warehouse?
ClickHouse for OLAP speed, Postgres for serving; Iceberg for lakehouse.
How is quality enforced?
Great Expectations suites in CI; alerts on drift and volume anomalies.
Do you handle PII?
Yes - masking, access policy and governance pack add-on.
Related services
Pairs well with.
Data Engineering Services without the guesswork.
Pipelines and warehouses you can trust: tested, documented, observable.