AI-Driven Data Quality Analyst

PSL Group; Santiago de Querétaro; On-site; MXN 420,000 - 660,000; Full time

Job summary

P\S\L Group is seeking a data quality professional in Querétaro to own data quality monitoring, investigations, and pre-deployment testing. You design automated checks, set alert baselines, and collaborate with data engineering and AppDev teams to ensure data integrity across datasets and releases.

The role requires strong SQL, experience with monitoring tools (dbt, Great Expectations, Soda), and familiarity with AI coding tools. English proficiency is essential; Spanish is a plus.

Qualifications

• 3+ years in data quality, data analysis, data QA or analytics engineering.
• Strong SQL: comfortable comparing large datasets, writing reconciliation queries and profiling data.
• Hands-on experience setting up data monitoring or alerting (dbt tests, Great Expectations, Soda, or custom SQL/Python checks).
• Hands-on experience with AI coding tools like Claude code, OpenAI Codex CLI, Antigravity CLI, or similar.
• Ability to articulate and write requirements for automations, query schedules, and data quality alerts.
• Experience testing data around software releases, ideally working alongside application developers.
• Methodical root-cause analysis and clear written documentation.
• Strong attention to detail
• Confident coordinating with people across teams and time zones without direct authority.
• Professional working proficiency in English, written and spoken; Spanish an asset.
• Able to work with significant overlap with Eastern Time (Toronto).

Responsibilities

• Own data quality monitoring (about 40%).
• Design, build and maintain automated checks on key activity and reference datasets: volumes, rates, freshness, completeness and distribution shifts.
• Set baselines and alert thresholds so the team is told about unexpected changes — for example, a sudden drop in clinicians opening emails, a spike in bounces, or missing specialty values.
• Tune alerts to keep noise low; every alert should be worth looking at.
• Maintain a data quality dashboard and report trends and recurring issues in the team's monthly metrics.
• Own data quality investigations (about 30%).
• Triage each alert or reported issue: confirm it is real, size the impact and set a priority.
• Start the investigation and pull in the right people — data engineering, AppDev, the data architect, reference data owners and business stakeholders.
• Trace issues to root cause across source systems, pipelines and reference tables.
• Document findings, impact and resolution in a shared issue log and in Confluence, so recurring issues become known patterns.
• Communicate status clearly to affected stakeholders until the issue is closed.
• Pre-deployment Data Acceptance Testing (about 30%).
• Collaborate with Application Development and data engineering teams on which datasets each release should and should not affect.
• Run pre- and post-deployment data comparisons (row counts, key metrics, schema and value checks) to confirm only expected data changes.
• Give formal data sign-off, or raise a blocker, for releases - particularly AppDev deployments where downstream data impact is not expected.
• Turn repeat checks into reusable, automated test suites over time.
• Feed lessons from incidents back into release checklists and change-request reviews.

Skills

Data quality

SQL

Data monitoring

AI coding tools

Requirements writing

Data testing

Root-cause analysis

Attention to detail

Cross-team coordination

English proficiency

Tools

dbt tests

Great Expectations

Soda

Python

Jira

Confluence

Zoho CRM

Job description

P\S\L Group is seeking a data quality professional in Querétaro to own data quality monitoring, investigations, and pre-deployment testing. You design automated checks, set alert baselines, and collaborate with data engineering and AppDev teams to ensure data integrity across datasets and releases.

The role requires strong SQL, experience with monitoring tools (dbt, Great Expectations, Soda), and familiarity with AI coding tools. English proficiency is essential; Spanish is a plus.