Senior Data Analyst (Contract)

K2 Integrity

About The Position

We are looking for a seasoned Senior Data Analyst to join our Financial Crimes / Anti-Money Laundering (AML) practice. This is a high-impact role at the intersection of data science and financial crime compliance, where your analytical work will directly support investigations, strengthen detection capabilities, and help protect clients from financial risk. You'll collaborate closely with investigations teams, translating complex data into clear, actionable intelligence.

Requirements

Advanced degree in a related field (e.g., Data Science, Statistics, Finance)
7+ years of hands-on, production-level SQL coding — complex queries, stored procedures, and performance-tuned scripts against large financial datasets
Experience in financial crimes, AML, or a closely related field
Hands-on Python coding for data wrangling, automation, statistical analysis, and financial crime detection logic
Robust ETL capabilities, ideally with SSIS, Azure Data Factory (ADF), Databricks or Alteryx
Strong Power BI skills for interactive dashboards, data models, and executive-level reporting; Tableau experience is a plus
Familiarity with cloud platforms (preferably Azure) and working with AML data infrastructure in cloud environments
Strong understanding of AML typologies such as structuring, layering, and trade-based money laundering
Excellent communication skills, able to present complex findings clearly to non-technical stakeholders and team members
Ability to self-manage multiple workstreams independently in a fast-paced, client-facing environment
Strong mentorship instincts with a track record of elevating junior team members
Prior experience at a major financial institution or financial crimes consulting firm
Experience with Alteryx for data blending, workflow automation, and advanced analytics
Familiarity with major AML platforms such as Actimize, Oracle FCCM, or NICE

Responsibilities

Analyze transactions, accounts, customer profiles, alerts, and third-party data to identify suspicious patterns, anomalies, and emerging risks
Write and optimize complex SQL queries and Python scripts to extract, manipulate, and analyze large financial datasets hands-on
Design and implement financial crime detection models, scenarios, and rule sets tailored to AML typologies
Conduct root cause analyses on financial crime incidents to improve detection accuracy and prevention strategies
Build and maintain ETL pipelines to ingest, clean, and validate data from multiple sources
Develop clear, compelling dashboards and reports in Power BI to support investigator decision-making and stakeholder reporting
Apply AI/ML techniques including supervised and unsupervised models, anomaly detection, and NLP — to enhance detection efficiency and surface emerging risks
Leverage graph analytics to map and analyze relationships between entities, accounts, and transactions
Translate forensic data analyses into findings and recommendations that enable effective, timely investigations
Mentor junior analysts and contribute to building overall team capability