Business Intelligence
One version of the numbers, available when decisions get made.
Cloud, Data & Reliability
The unglamorous layer everything else depends on
Analytics, BI and machine learning all fail the same way: the data arrived late, incomplete, or subtly wrong, and nobody found out until a decision had been made on it.
- Engagement
- Project build then optional platform retainer
- Typical timeline
- 6–14 weeks
- Delivered across
- All 50 US states
Overview
Data engineering is infrastructure work. It gets attention only when it breaks, which is exactly why it deserves engineering discipline rather than a collection of scheduled scripts on someone's machine. Pipelines need version control, testing, monitoring, retry logic and clear ownership — the same standards any production system requires.
We build pipelines that ingest from your operational systems, transform data into modeled form, test it for quality before publishing, and alert when something is wrong. Incremental where volume demands it, idempotent so reruns are safe, and documented so the next engineer can follow what happens and why.
We are pragmatic about scale. Most mid-market companies do not need streaming architecture or a data lakehouse. They need reliable batch pipelines into a warehouse, done properly. We size the architecture to your actual data volume rather than to a conference talk.
- Quality checks before data is published
- TestedQuality checks before data is published
- Safe to rerun without duplicating
- IdempotentSafe to rerun without duplicating
- Freshness and volume alerting on every source
- MonitoredFreshness and volume alerting on every source
Business benefits
What data engineering changes for you
The reasons this work earns its budget, stated as outcomes rather than features.
Reliable ingestion
Connectors to your operational systems with incremental loading, retry logic and schema change handling.
Transformation in version control
Modeling as tested, reviewed code with lineage documentation, not SQL living in a BI tool nobody can audit.
Quality tested before publication
Automated checks on freshness, uniqueness, referential integrity and business rules that block bad data from reaching reports.
Monitored with real alerting
Pipeline failures, late data and volume anomalies alert a named owner rather than being discovered in a meeting.
Costs kept sensible
Incremental processing, partitioning and appropriate storage tiers so warehouse spend scales with value rather than volume.
Sized to your reality
Architecture matched to actual data volume and team capability, not to the largest system we could justify building.
Problems solved
If any of this sounds familiar
These are the situations clients describe in the first conversation, and what we do about each.
“Our data pipelines break constantly and silently.”
Rebuild with proper error handling, retries, idempotency and alerting so failures are visible, recoverable and rare.
“Analysts spend most of their time cleaning data.”
Move cleaning and modeling upstream into tested pipelines, so analysts consume trusted data and spend time on analysis.
“Our warehouse costs are climbing sharply.”
Incremental processing, partitioning, query optimization and storage tiering, which usually reduces cost substantially without losing capability.
“We need clean data for an AI project and do not have it.”
Build the pipeline and quality layer first. Data readiness is the most common reason AI projects stall before they start.
Our process
How we deliver it
Each stage has a defined output, so you always know what you are getting and when.
- 01
Source assessment
Inventory systems, data volumes, update patterns, quality issues and access methods for each source.
- 02
Architecture
Warehouse, pipeline and orchestration design sized to real volume, with a clear modeling approach.
- 03
Pipeline development
Ingestion and transformation built as tested, version-controlled code with lineage documentation.
- 04
Quality and monitoring
Data quality tests, freshness monitoring and alerting configured before downstream consumers depend on the data.
- 05
Operate and extend
Ongoing reliability, cost review and incremental addition of sources as requirements grow.
Technologies used
The tools behind the work
Chosen for maintainability and hiring depth rather than novelty. We will justify any choice on request, and we avoid technology that makes you dependent on us.
- dbt
- Airflow
- Fivetran
- Airbyte
- Snowflake
- BigQuery
- Postgres
- Python
- Dagster
- Kafka
Industries served
Where this work lands most often
Sector context changes what good looks like. These are the industries where we have delivered this service repeatedly.
Why Mova
What working with us on data engineering is like
The same commitments apply to every engagement, regardless of size or service.
A defined first step
We scope a fixed-price starting point so you can evaluate us on data engineering before committing to a program.
Senior people, named
The team you meet is the team that delivers. You will know exactly who is accountable.
You own the output
Code, accounts, files and documentation are yours from day one, with no lock-in of any kind.
Measured, then reported
We baseline before starting and report against it honestly — including the months that fall short.
Questions
Data Engineering: common questions
The questions we are asked most about data engineering, answered directly.
ELT for most modern warehouses — load raw data, transform inside the warehouse where compute is cheap and transformations are auditable. ETL still makes sense when data must be filtered or masked before it lands for compliance reasons.
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Next step
Ready to talk about data engineering?
Thirty minutes with someone who has delivered this work. We will tell you what it would take, roughly what it would cost, and whether Mova is the right fit.
No pitch deck. A 30-minute conversation about what you are trying to achieve.