Data Quality Analyst

Data Quality Analyst

Our Vision

P\S\L Group is a global organisation dedicated to putting information at the service of medicine.  The companies and people of the P\S\L Group aim to improve medical care by serving those who need it, those who provide it, and those who seek to improve it.

To this end, we want our information and education services to contribute to the goals we share with our clinicians, clients and supporters, namely: to accelerate the advancement of medicine and help people enjoy better, longer lives. 

Purpose

Our key contribution to society is to help clinicians and those who support them provide state-of-the-art medical care. 

Our primary business purpose is to help clients and supporters increase the effectiveness of activities pertaining to scientific communication, medical education and the maintenance of clinician audience intimacy.

Key responsibilities

1. 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.

2. 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.

3. 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.

Qualifications

Required

• 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).

Preferred

• Python for automation and anomaly detection.
• Experience with marketing or engagement data (email sends, opens, clicks, bounces), ideally in healthcare or pharma communications.
• Familiarity with reference data, master data or data governance practice (e.g., DAMA-DMBOK).
• Experience using Jira/Confluence, google suite, 
• Knowledge of CRM data (e.g., Zoho) and privacy-aware handling of HCP data.

How you work

• Takes ownership and drives issues to closure on their own.
• Stays calm and structured when the problem is ambiguous. Breaks down tasks into manageable chunks and iterates.
• Prefers automating a check over repeating it by hand.
• Flags what they find early and plainly, even when the news is unwelcome.
• Uses AI to help you get things done faster.