Data Quality and Data Observability Services
Catch broken, late or wrong data before it reaches reports, customers or AI.
Stack we work in
dbt- dbt Cloud
- Unity Catalog
- Snowflake
- Databricks
Microsoft Fabric- PostgreSQL
- Apache Airflow
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs your data quality needs attention
Data quality and observability services make sure the numbers your business relies on are correct, complete and on time, and that someone knows within minutes when they are not.
Read the full overview
We add automated tests to your pipelines with dbt tests, Great Expectations and Soda, set up observability with Elementary, Monte Carlo or your cloud's tools, and track freshness, volume, schema changes and anomalies across your warehouse or lakehouse. Lineage shows which reports and models a problem affects, data contracts stop bad changes at the source, and agreed data SLAs give each important dataset an owner and a target. The result is fewer surprises in board reports and faster fixes when something breaks.
Users find the errors first
Business users spot wrong numbers in reports before the data team knows anything is broken.
Silent pipeline failures
A job fails or loads half the data, and nobody notices until the next meeting.
Source changes break everything
An application team renames a column and a week of reports breaks.
No idea what a fix affects
When something breaks, nobody can say which dashboards or models depend on it.
Reports people double-check
Teams export data to spreadsheets to check figures because they do not trust them.
Outcomes
What you get
- Problems caught by tests and alerts before business users see them
- A named owner and an SLA for every critical dataset
- Lineage that shows which reports a failure affects
- Schema changes at the source flagged before they break pipelines
- A quality scorecard leadership can check at a glance
What we deliver
What you receive
Critical data inventory
The datasets and reports that matter most, with owners and agreed SLAs.
Tests as code
dbt tests, Great Expectations or Soda checks in source control and run in CI/CD.
Observability monitors
Freshness, volume, schema and anomaly monitors on critical tables.
Alerting and lineage
Alerts routed to owners, with lineage that shows the downstream impact.
Quality scorecard
A dashboard of test results, SLA performance and incidents over time.
Incident runbooks
A simple process for triage, fixes, communication and post-incident review.
Data testing
- dbt tests and unit tests for transformations
- Great Expectations and Soda checks
- Reconciliation against source systems
- Tests in CI/CD so bad changes never reach production
- Validation rules for business-critical fields
Built with
- dbt
- Great Expectations
- Soda
Observability & monitoring
- Freshness, volume, schema and distribution monitoring
- Elementary, Monte Carlo or Anomalo setup
- Anomaly detection on key metrics
- Alert routing to Slack, Teams or PagerDuty with clear owners
Built with
- Elementary
- Monte Carlo
- Anomalo
- Slack
- PagerDuty
Lineage & impact analysis
- Column-level lineage with dbt, OpenLineage or your catalog
- Impact analysis: which reports and models a failure affects
- Root-cause tracing back to the source
- Catalog integration with Unity Catalog, Purview, Atlan or DataHub
Built with
- dbt
- OpenLineage
- DataHub
- Atlan
- Unity Catalog
Data contracts & SLAs
- Data contracts between producing and consuming teams
- Freshness and accuracy SLAs for critical datasets
- Quality scorecards and dashboards
- Incident process, ownership and post-incident reviews
- Quality checks on data used for AI and machine learning
Why teams choose Equitysoft for data quality and observability
The lightest tool that works
We start with dbt tests and open-source tools and only recommend a paid observability product where it pays back.
Owners, not just alerts
Every critical dataset gets a named owner and an SLA, and alerts go to the person who can fix the problem.
Critical data first
Tests and monitors go on the datasets behind key reports first, then coverage widens.
Checks in your workflow
Tests run in CI/CD and alerts reach Slack, Teams or PagerDuty. 50+ engineers, 20+ of them certified on cloud and data platforms, delivering since 2018.
How we work
Assess
We list the datasets and reports that matter most and review recent data incidents.
You receive Critical data inventory and incident review
Design
We agree the checks, thresholds, owners and tools that fit your stack and budget.
You receive Quality rules, SLAs and tool choice
Instrument
dbt tests, Great Expectations or Soda checks and observability monitors are added as code.
You receive Tests and monitors in your pipelines
Connect
Alerts go to the right owners, and lineage links failures to affected reports.
You receive Alerting, lineage and catalog
Embed
We set up the on-call process, runbooks and a quality scorecard.
You receive Incident process and scorecards
Improve
We reduce noisy alerts and extend coverage to more datasets over time.
You receive Tuned thresholds and wider coverage
Tech stack
Technologies we use
Data platforms
- Unity Catalog
- Microsoft Purview
- Snowflake
- Databricks
- Google BigQuery
- Amazon Redshift
- Microsoft Fabric
Pipelines and orchestration
- dbt
- dbt Cloud
- Great Expectations
- Apache Airflow
- Dagster
- Prefect
Databases
Languages
Cloud and DevOps
Messaging and channels
Other tools
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
Data quality checks test known rules, such as "order total is never negative" or "every customer has an email". Data observability watches for the unexpected: data that arrives late, row counts that suddenly drop, or a column whose values shift. You need both.
dbt tests, Great Expectations and Soda for rule-based checks; Elementary for dbt-native observability; Monte Carlo, Anomalo or Bigeye for managed observability; and OpenLineage, DataHub, Atlan or your platform's catalog for lineage. We match tools to your stack and budget.
Not always. Many teams get far with dbt tests, Elementary and alerts to Slack or Teams. Paid tools such as Monte Carlo add automatic anomaly detection and wider coverage, which pays off with many sources and teams. We recommend the lightest setup that covers your risks.
A data contract is an agreement between the team that produces data and the teams that use it: the schema, meaning, freshness and quality to expect. Contracts are checked automatically, so a change at the source that would break reports is caught before release.
A data SLA is a target for a dataset, such as "sales data is loaded by 7am with fewer than 0.1% rejected rows". Each SLA has an owner, a monitor and an alert, so missed targets are visible and fixed.
We start with a small set of high-value checks, route alerts to named owners, group related failures, and tune thresholds over the first weeks. Every alert should lead to an action.
Yes. We add tests and monitors to the pipelines you already run on Snowflake, Databricks, BigQuery, Redshift, Fabric or PostgreSQL, without rebuilding them.
Lineage shows where the data came from and where it goes. When a check fails, you can see which dashboards and models are affected and trace the problem back to the source quickly.
Very much. Models and AI assistants repeat the errors in their data. We add checks for completeness, duplicates, freshness and drift on the data used for training, features and retrieval.
A first set of tests, monitors and alerts on your most critical datasets usually takes 3 to 6 weeks. Wider coverage, contracts and SLAs follow in phases.
It depends on the number of pipelines, datasets and tools involved. Tool licenses are billed to you directly. As a guide, a quality and observability setup on your most critical data usually costs $6,000 to $20,000, and a platform-wide program with contracts and SLAs costs $20,000 to $50,000 or more (USD).
We start by listing the datasets behind your most important reports and the incidents of the last few months. We need read access to the warehouse and pipelines and a named owner per critical dataset. After setup we hand over runbooks or stay on to tune thresholds and widen coverage.
More about our Data Quality & Observability
Working togetherHow we work with your team
Critical data first
We start with the few datasets that drive key decisions, not every table at once.
Lightest tool that works
Open-source tools first, paid tools only where they clearly earn their cost.
Owners, not just alerts
Every check has a named owner and a clear action, so alerts get fixed.
Part of your workflow
Checks run in your existing pipelines and CI/CD, reviewed like any other code.
Where data quality matters most
Financial services
Accurate, complete data for regulatory reports, risk models and reconciliations.
Healthcare
Correct patient and clinical data, where errors carry real risk.
Retail and ecommerce
Reliable stock, pricing and order data across channels and marketplaces.
SaaS
Trustworthy usage and billing data for customer reports and revenue figures.
Talk to a data quality engineer
Tell us which reports people do not trust and where data has let you down.