Curious about what it’s like to work at Cognyte?

At Cognyte, you’ll collaborate with expert colleagues around the globe to solve problems most people will never even know exist!

You’ll be part of building unique solutions shaped by real investigative methodologies, enabling our customers to identify, investigate, visualize and prevent criminal, terror and security threats worldwide.

These solutions are used by law enforcement, national security, and national and military intelligence agencies in almost 100 countries to turn massive, diverse data into clear, actionable intelligence for a safer world.

We are looking for a sharp, analytical, and detail-oriented Data Analyst with a strong finance background who enjoys investigating complex financial data problems, working with large transactional datasets, and digging deeper than the obvious answer.

As a Cognyter, you will:

• Analyze complex financial data using SQL to uncover trends, anomalies, and actionable insights.
• Investigate data quality issues and perform root cause analysis on discrepancies and reconciliation breaks.
• Build and validate matching, reconciliation, and data validation processes.
• Develop and maintain BI dashboards and reports for business stakeholders.
• Partner with Product, Engineering, and business teams to solve data-driven challenges and automate recurring analyses.

For this mission, you’ll need:

• 3+ years of experience as a Data Analyst working with financial, banking, fintech, payments, or similar datasets.
• Strong SQL skills and experience analyzing large, complex datasets.
• Solid understanding of financial concepts, including transactions, accounts, reconciliation, and key metrics.
• Experience with Python (Pandas) and BI/data visualization tools.
• Strong analytical, problem-solving, and communication skills, with the ability to leverage AI tools effectively and validate results.

Nice to Have

• Experience with AML, fraud, risk, financial crime, or reconciliation analytics.
• Familiarity with OSINT and third-party data sources.
• Experience with technologies such as ClickHouse, Trino, Kafka, Spark, or Iceberg.
• Experience with Apache Superset or similar BI platforms.
• Knowledge of data warehouses, ETL processes, and Git.