Operational Risk - Data Analyst, Sr - Fraud & Risk

OnPoint Credit Union•Portland, OR

14h

About The Position

The Data Analyst Sr. – Fraud & Risk will be responsible for transforming fragmented fraud and risk data into trusted, actionable insights that accelerate fraud detection, investigation, recovery, risk mitigation, and audit readiness. This role operates in an ML‑enabled vendor environment, partnering closely with Fraud Operations, Risk, and Technology to extract maximum value from platforms like Verafin while laying the data foundation for future in‑house modeling. The analyst will also perform risk data analysis across fraud patterns, operational controls, exposure trends, and model performance to strengthen organizational decision‑making.

Requirements

Advanced SQL proficiency, including complex joins, window functions, data cleansing, performance tuning.
Strong proficiency with cloud data platforms, such as Databricks, Delta Lake, and distributed data processing.
Proven experience with trend analysis, loss/risk modeling, exposure calculations, scenario analysis.
Deep expertise in data quality and governance, such as data lineage, metric definitions, documentation, and standardization.
Expert-level knowledge in dashboard and visualization tools, executive reporting, investigator tools, KPI dashboards.
Working knowledge of data engineering fundamentals, including data modeling, pipeline development, ETL/ELT, and building curated datasets.
Ability to break down complex data issues, identify root causes, and propose actionable solutions.
Strong ability to convert data into meaningful operational and risk insights that influence decisions.
Strong written and verbal communication, with the ability to communicate complex technical issues to employees at all levels.
Ability to collaborate effectively with cross-functional teams, including senior leaders.
Working knowledge of fraud schemes, typologies, transaction flows, loss drivers, and enterprise risk frameworks.
Knowledge of ML‑based alerting systems, risk scoring, thresholding, and model performance concepts.
Strong attention to detail, ensuring accuracy, traceability, and defensibility of analyses and reporting.
5 years of experience in data analytics
Advanced SQL and experience working with large, messy datasets
Experience with Databricks or modern cloud data platforms
Proven ability to translate analytics into operational and executive insights

Nice To Haves

Knowledge of python/scripting, including data transformation, automation, and analytics (preferred but not required).
8 years of experience in data analytics, preferably in financial services, risk, or fraud
Strong understanding of fraud concepts (alerts, cases, typologies, losses, recovery)
Experience working with vendor fraud platforms (e.g., Verafin)
Familiarity with model outputs, thresholds, and false‑positive tuning
Experience supporting audits or regulatory exams
Experience with Python or similar scripting for data transformation

Responsibilities

Design and maintain fraud and risk analytics datasets in Databricks sourced from Verafin, Accelerant, Q2, Fed files, and other operational systems.
Build governed, repeatable pipelines and curated tables to replace manual Excel/macros processes.
Integrate cross‑channel fraud and risk views, such as member, account, product, transaction, case, exposure, or control effectiveness.
Perform risk data analysis to identify trends, gaps, typologies, control failures, and emerging threats.
Conduct quantitative assessments of fraud exposure, loss forecasting, and risk scenario analysis.
Partner with Fraud Operations to interpret and operationalize vendor ML outputs, such as risk scores, alerts, typologies.
Analyze model performance, false‑positive rates, thresholds, and operational impacts on fraud and risk controls.
Collaborate with first‑ and second‑line risk teams to ensure model outputs align with broader risk strategies.
Develop executive‑ready dashboards and investigator‑ready reporting.
Support audit, compliance, and regulatory requests with defensible evidence, through traceable, well‑documented metrics.
Contribute to fraud and risk data standards, definitions, metadata, and documentation.
Collaborate with Technology and Architecture on data ingestion and quality improvements.
Additional projects, responsibilities, or other duties assigned.