Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Principal Data Analyst

Realtime Recruitment
Belfast
3 days ago
Create job alert

This range is provided by Realtime Recruitment. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

Direct message the job poster from Realtime Recruitment


Senior IT Recruitment Consultant at Realtime Recruitment

Ambitious, forward-leaning security organization operating at the cutting edge of technology. The mission is to protect customers, infrastructure, and digital assets through intelligent, data-driven decision making.


Role Overview

As a Senior Data Analyst, you will play a critical role in shaping the data strategy and transforming complex security data into actionable insights. You will partner with engineering, security operations, threat intelligence, and product teams to drive high-impact initiatives. This is a hands‑on individual contributor position with significant influence across the organization.


Key Responsibilities

  • Lead end-to-end analytical initiatives across security domains (e.g., threat detection, SIEM analytics, incident response, vulnerability management, identity security).
  • Build, optimize, and maintain scalable data pipelines and security datasets.
  • Apply advanced analytics and statistical methods to detect anomalies, uncover patterns, and support proactive threat mitigation.
  • Develop dashboards, reports, and analytical frameworks that enable real-time visibility into security posture.
  • Mentor analysts and collaborate with engineers to establish best practices for data quality, governance, and automation.
  • Partner with leadership to drive strategic decisions through quantitative analysis.
  • Translate ambiguous security problems into structured analytical approaches.

Required Qualifications

  • 6+ years of experience as a Data Analyst, Data Scientist, or similar analytical role.
  • Security industry experience (required) — e.g., SOC analytics, SIEM data, threat intel, log analysis, or cybersecurity product data.
  • Strong Python skills for data processing, automation, and analysis.
  • Expert SQL skills with experience across major relational databases (PostgreSQL, MySQL, Redshift, etc.).
  • Strong understanding of KPIs, metrics, statistical analysis, and data visualization.
  • Ability to communicate complex technical insights to both technical and non‑technical audiences.

Preferred Qualifications

  • AWS experience preferable.
  • Experience with security analytics tools (e.g., Splunk, Elastic, Chronicle, Snowflake security logs).
  • Familiarity with cloud security, threat detection, identity access analytics, or vulnerability data.
  • Experience building automated workflows and analytics in cloud ecosystems.
  • Background in BI tools (Tableau, Power BI, QuickSight, Looker).

Seniority level

Mid-Senior level


Employment type

Full-time


Job function

Information Technology


Industries

IT Services and IT Consulting


Referrals increase your chances of interviewing at Realtime Recruitment by 2x


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Analyst

Principal Data Analyst

Principal Data Analyst

Principal Data Analytics Software Developer

Principal Data Engineer

Principal Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.