Data Analyst, Senior - Fraud & Risk

OnPoint Credit Union•Portland, OR

About The Position

The Data Analyst Senior – Fraud & Risk transforms fragmented fraud and risk data into trusted, actionable insights that support fraud detection, investigation, recovery, risk mitigation, and audit readiness. The role works in an ML-enabled vendor environment, partnering with Fraud Operations, Risk, and Technology to maximize the value of platforms such as Verafin while building a data foundation for potential future in-house modeling. The analyst also evaluates fraud patterns, operational controls, exposure trends, and model performance to strengthen organizational decision-making. The position designs and maintains fraud and risk analytics datasets in Databricks using information from Verafin, Accelerant, Q2, Fed files, and other operational systems. It develops governed, repeatable pipelines and curated tables, integrates cross-channel fraud and risk views, analyzes risk and fraud trends, and assesses exposure, losses, and scenarios. The analyst interprets vendor ML outputs with Fraud Operations, evaluates model performance and operational impacts, creates dashboards and reporting, supports audit and regulatory requests with documented evidence, and contributes to data standards and quality improvements.

Requirements

At least 5 years of experience in data analytics.
Advanced SQL proficiency, including complex joins, window functions, data cleansing, and performance tuning.
Experience working with large, messy datasets.
Experience with Databricks or modern cloud data platforms.
Proven ability to translate analytics into operational and executive insights.
Strong proficiency with cloud data platforms, such as Databricks, Delta Lake, and distributed data processing.
Experience with trend analysis, loss and risk modeling, exposure calculations, and scenario analysis.
Expert-level knowledge of data quality and governance, including data lineage, metric definitions, documentation, and standardization.
Expert-level knowledge of dashboard and visualization tools, executive reporting, investigator tools, and KPI dashboards.
Working knowledge of data engineering fundamentals, including data modeling, pipeline development, ETL/ELT, and curated datasets.
Ability to break down complex data issues, identify root causes, and propose actionable solutions.
Ability to turn data into meaningful operational and risk insights that influence decisions.
Strong written and verbal communication skills, including 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 to ensure accuracy, traceability, and defensibility of analyses and reporting.

Nice To Haves

At least 8 years of experience in data analytics, preferably in financial services, risk, or fraud.
Strong understanding of fraud concepts, including alerts, cases, typologies, losses, and recovery.
Experience working with vendor fraud platforms such as Verafin.
Familiarity with model outputs, thresholds, and false-positive tuning.
Experience supporting audits or regulatory examinations.
Experience with Python or similar scripting for data transformation.
Knowledge of Python or scripting for data transformation, automation, and analytics.

Responsibilities

Design and maintain fraud and risk analytics datasets in Databricks using Verafin, Accelerant, Q2, Fed files, and other operational systems.
Build governed, repeatable pipelines and curated tables to replace manual Excel and macro processes.
Integrate cross-channel fraud and risk views, including member, account, product, transaction, case, exposure, and control-effectiveness information.
Analyze risk data to identify trends, gaps, typologies, control failures, and emerging threats.
Conduct quantitative assessments of fraud exposure, loss forecasting, and risk scenarios.
Partner with Fraud Operations to interpret and operationalize vendor ML outputs, including risk scores, alerts, and 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 align model outputs with broader risk strategies.
Develop executive-ready dashboards and investigator-ready reporting.
Support audit, compliance, and regulatory requests with defensible evidence and traceable, well-documented metrics.
Contribute to fraud and risk data standards, definitions, metadata, and documentation.
Collaborate with Technology and Architecture on data ingestion and data quality improvements.
Perform additional projects, responsibilities, or other assigned duties.