Data Analyst

ABOUT THE TEAM

We are building out the Data Analytics function within Nebius's CISO department. The team currently includes a senior BI Developer focused on Tableau dashboards, and we are now looking for a Data Analyst to complement that work with deeper analytical investigation. The department's scope spans both cybersecurity risk and broader operational and PMO metrics, and you will work directly with security, GRC, PMO, IT, and leadership stakeholders to answer data questions, uncover patterns, and support better decision-making across the department.

THE ROLE

We are looking for a Data Analyst with 2-4 years of experience who is curious, analytical, and comfortable working with imperfect or unfamiliar data. This role is focused on understanding data deeply and turning it into meaningful insights. You will explore complex datasets, investigate the drivers behind changes in key metrics, and help stakeholders make better decisions across security operations, risk, compliance, PMO, and leadership.

WHAT YOU'LL DO

Analyze large, complex datasets to identify patterns, anomalies, and root causes, and turn them into actionable insights.

Investigate changes in key metrics, form hypotheses, and run structured deep-dives to identify underlying drivers.

Segment and compare data across dimensions (distributions, percentiles, baselines) to uncover meaningful differences and outliers.

Research and assess new data sources and APIs from an analytical perspective: what is available, its quality and limitations, and the analytical value it could unlock.

Use SQL to extract, join, and analyze data from multiple sources.

Use Python (e.g., pandas) for exploratory analysis, data manipulation, and automating recurring analyses.

Help define and refine KPIs and metrics together with stakeholders, contributing analytical input alongside the BI team's dashboard work.

Apply the same analytical rigor to both cybersecurity risk metrics and operational/PMO KPIs (e.g., program delivery, resourcing, process efficiency).

Use AI-assisted tools to speed up exploratory analysis, coding, and documentation, while validating and owning the resulting outputs.

Identify data-quality issues and inconsistencies that may affect analysis, and work with the relevant teams to resolve them.

Create lightweight visualizations and analytical outputs to communicate findings clearly.

Present insights, conclusions, and recommendations to technical and non-technical stakeholders.

REQUIREMENTS

Must have:

2-4 years of professional experience as a Data Analyst or in a similar hands-on analytical role.

Strong SQL skills: joins, CTEs, window functions, aggregations, working across multiple data sources.

Hands-on Python experience for data analysis, including libraries such as pandas (e.g., building repeatable analysis notebooks or automating a recurring report).

Strong exploratory data analysis skills and comfort working with unfamiliar or messy datasets.

Ability to identify trends, anomalies, and outliers, and to investigate the drivers behind changes in metrics through root-cause analysis.

Strong attention to data quality and the discipline to validate findings before drawing conclusions.

Ability to turn ambiguous business questions into structured analytical approaches.

Clear communicator, comfortable presenting findings and recommendations to non-technical audiences.

Strong written and verbal English.

Security domain - strong advantage

Experience working in a CISO, SOC, GRC, cybersecurity, or security operations environment.

Familiarity with security metrics such as vulnerability management, incident response, access management, risk, or compliance KPIs.

Experience analyzing data from security tools such as SIEM, EDR, IAM, vulnerability management, or ticketing platforms.

Knowledge of frameworks such as ISO 27001, NIST CSF, or SOC 2.

Nice to have

Familiarity with statistical methods used in exploratory analysis and anomaly detection.

Experience with Tableau, Power BI, or another BI/visualization platform.

Experience with cloud platforms such as Azure, AWS, or GCP.

Familiarity with data transformation concepts or tools such as dbt.

Comfort using AI/LLM-based tools to accelerate analysis, coding, or documentation.