For our client, we are looking for a Data Quality / Test Engineer.

The Data Products team owns a portfolio of core data warehouses on Snowflake + dbt:

• SDW — Sales Data Warehouse
• OIDW — Order and Inventory Data Warehouse
• MDW — Manufacturing Data Warehouse
• SQE — Sustainability, Quality, and Environmental Data Warehouse

Each warehouse has a set of core downstream applications and warehouses consuming its

data. We are intensifying data quality evaluation across this portfolio for two reasons:

1. (WP1) The recent SDW v2.0 upgrade introduced data quality issues that surfaced

downstream and demand a structured remediation lens.

2. (WP2) We anticipate future extensions to OIDW, MDW, and SQE. Embedding tests inside

these warehouses gives us a regression safety net so changes can ship without breaking

downstream consumers.

This SOW engages a single vendor resource over six months to support both efforts in a

review and investigation capacity. Tests are designed by the resource and implemented by

the Data Products team. Tests live inside the warehouse projects (dbt tests in each

warehouse's dbt repo) and validate data on the warehouse side, before it reaches downstream

consumers. Downstream application test suites are not touched.

Work Package 1 — SDW (Automotive SDW 2.0)

Duration: ~12 weeks. Priority: Primary focus at the start of the engagement.

Objectives

1. Map and confirm how SDW 2.0 data is consumed by the core known downstream

systems, e.g. DCBI, OIDW, ...

2. Make the implicit data expectations of each downstream consumer explicit, captured as a

per-warehouse data contract.

3. Produce a prioritized catalog of data quality tests, ready for implementation by the Data

Products team inside the SDW dbt project.

4. Identify risks and coverage gaps, with particular attention to the patterns that drove SDW

v2.0 regressions.

Deliverables

1. Downstream usage inventory — per consumer: SDW tables/columns used, refresh

cadence, criticality, observed v2.0 pain points.

2. Data contract for SDW — the set of guarantees SDW makes to its core consumers,

organized by SDW table/column area, with consumer references annotated. Each entry is

the justification for one or more entries in the test catalog.

3. Prioritized test case catalog — concrete, implementable test specifications targeting SDW

tables/columns (table / column / rule / severity / dependent consumer(s)). Specifications

only; no code. Test severity ties to consumer criticality.

4. Risk and gap report — where consumer contracts conflict, where coverage is thin, where

v2.0-style regressions are most likely to recur, and which planned SDW changes are most

likely to violate which contracts.

Phasing

Phase 1: Inventory and stakeholder interviews

Phase 2: Data contract and test catalog design

Phase 3: Risk and gap report; handoff

Phase 4: Clarifications, revisions, knowledge transfer

Each phase deliverable is signed off by the Head of Data Products (or delegate) before the next

phase begins. The swap decision checkpoint sits at the end of Phase 2.

Work Package 2 — OIDW, MDW, SQE

Duration: ~12 weeks. Starts: Immediately after WP1 completion (approximately midSeptember 2026, subject to WP1 progress).

Objectives

1. Map and confirm how OIDW, MDW, and SQE data is consumed by their respective core

known downstream systems.

2. Capture a data contract per warehouse — the set of guarantees each warehouse makes to

its core consumers.

3. Produce prioritized test case catalogs targeting each warehouse's dbt project, so that the

Data Products team can implement them as guard rails for future warehouse extensions.

4. Identify cross-warehouse risks and coverage gaps in a single consolidated report.

Deliverables

1. Cross-warehouse downstream usage inventory — consumers mapped to OIDW, MDW,

and SQE. Shared consumers (applications that consume from multiple warehouses, or that

already appeared in the SDW inventory) are captured once with cross-references.

2. Data contract per warehouse — one document each for OIDW, MDW, and SQE.

3. Prioritized test case catalog per warehouse — targeting each warehouse's

tables/columns; specifications only; no code; severity tied to consumer criticality.

4. Consolidated cross-warehouse risk and gap report — one document covering OIDW,

MDW, and SQE, calling out patterns and recurring risks across the three warehouses.

Phasing

Sequencing across the three warehouses (parallel discovery vs. sequenced deep-dives,

ordering of warehouses) will be decided at the start of WP2, informed by WP1 outcomes and

Data Products team prioritization. The expected shape is a parallel discovery sprint up front (to

amortize interview costs across shared consumers), followed by sequenced per-warehouse

deep-dives and a single consolidated risk report.

Methodology and templates from WP1 are reused.

Phase deliverables are signed off by the Head of Data Products (or delegate) before the next

phase begins.

Engagement summary

Role: Data Quality / Test Engineer (hybrid analyst and tester)

Capacity: Single resource, full-time, six months, split across two work packages

Working model: Fully remote; best-cost countries

Target start: Mid-June to early July 2026, aligned with the Data Products team taking over ownership of SDW 2.0 for Automotive

Reporting line: Head of Data Products; embedded in the Data Products team

Internal support available across both work packages: Data engineers, analytics engineers, data product managers, and warehouse subject-matter experts who will alsobroker introductions to downstream owners

Candidate requirements

Applies to the resource covering either or both work packages.

Must-have

• 3–8 years of experience in data quality, data testing, or data analytics engineering roles
• Advanced SQL on Snowflake
• Hands-on experience with dbt, including reading and tracing lineage across dbt models, and authoring dbt tests
• Experience tracing data lineage across multi-hop pipelines
• Conceptual familiarity with at least one data quality framework (Great Expectations, dbt tests, Soda, Monte Carlo, Bigeye)
• Demonstrated experience leading requirements / discovery conversations with technical and semi-technical data consumers
• Strong written communication — deliverables are documents read by people who were not in the interviews
• Business-fluent English (written and spoken)

Strong plus

• Prior engagement at our organization or familiarity with any of SDW, OIDW, MDW, SQE
• Experience with SQL Server (used by some downstream consumer systems)
• BI tool literacy (Power BI)

Priority

Data quality testing skill is the floor. Prior knowledge of our environment is a strong tiebreaker,

provided the testing requirements above are also met.

Out of scope

• Implementation, execution, or maintenance of tests (design only — the Data Products team implements in the warehouse dbt repos).
• Modification or design of downstream application test suites. The resource's work product targets the warehouse repos only.
• Generic warehouse-internal integrity tests not tied to a documented contract. Internal hygiene is the Data Products team's responsibility.
• Cross-warehouse re-architecture or contract harmonization. Inconsistencies between warehouses are flagged in the risk and gap report but not resolved.
• Remediation of SDW v2.0 defects or any changes to SDW, OIDW, MDW, or SQE themselves.
• Long-tail / one-person-project downstream consumers.
• Data quality tooling selection or procurement.
• Ongoing data quality monitoring or on-call.
• Investigation upstream of the target warehouses (i.e., source systems feeding them), except where directly relevant to a downstream contract issue.