Business Unit:

Cubic Transportation Systems

Company Details:

When you join Cubic, you become part of a company that creates and delivers technology solutions in transportation to make people's lives easier by simplifying their daily journeys, and defense capabilities to help promote mission success and safety for those who serve their nation. Led by our talented teams around the world, Cubic is committed to solving global issues through innovation and service to our customers and partners.

We have a top-tier portfolio of businesses, including Cubic Transportation Systems (CTS) and Cubic Defense (CD). Explore more on Cubic.com.

Job Details:

Job Summary

The Principal Data Analyst is a senior technical leader within CTS's Global Analytics function, acting as the subject matter expert (SME) for data, metrics, and analytical solutions. This role owns the design, integrity, and evolution of analytical data solutions, working closely with Engineering, Services, and Product teams to ensure data platforms, reporting models, and KPI frameworks are robust, scalable, and operationally meaningful.

The Principal Data Analyst leads tasks such as root cause analysis (RCA), impact assessments, and monitoring solution design for critical operational domains such as Ridership, Sales, Settlements, Device Performance, and Service Operations, ensuring insights directly drive service performance, contractual outcomes, and customer confidence. This role is also accountable for transforming complex operational, performance, and customer datasets into actionable insights that drive service excellence, contractual KPI/SLA compliance, predictive decision-making, and continuous improvement.

Key Responsibilities

The following are the key responsibilities of the position. It is expected that most, if not all of these, are met by the candidate:

Data Solution Design & Architecture

Lead the design of analytical data solutions, including data models, semantic layers, KPI frameworks, and reporting architectures.
Partner with Data Engineering and Platform teams to define:

Source-to-target mappings
Metric calculation logic
Data quality and reconciliation controls

Act as a design authority reviewer for analytics-related changes in data pipelines, schemas, and reporting models.

Engineering & Services Bridge

Serve as the primary data SME interfacing between:

Engineering (data platforms, ingestion, pipelines)
Services & Operations (field services, call centers, settlements, performance assurance)

Translate operational and contractual requirements into clear data and reporting designs.
Validate that engineering outputs support:

RCA
KPI traceability
Auditability
Contractual defensibility

Root Cause Analysis & Impact Assessment

Lead complex end-to-end RCA across multi-system datasets (transactions, devices, incidents, telemetry).
Perform impact analysis to quantify:

Ridership loss
Revenue and settlement exposure
SLA/KPI breaches
Customer and service impacts

Develop reusable RCA frameworks, templates, and datasets to accelerate investigation cycles.

Monitoring & Operational Intelligence

Design and own monitoring solutions for key operational metrics, including:

Ridership and patronage trends
Sales and settlement integrity
Device availability and health
Transaction success and latency

Define thresholds, alerts, and exception logic aligned to operational risk.
Ensure monitoring outputs are actionable, not just descriptive.

Advanced Analytics & KPI Ownership

Own and evolve KPI definitions, calculation logic, and governance alignment.
Lead enhancements to CTS's KPI Engine and performance frameworks.
Ensure KPIs are:

Traceable to source data
Reproducible
Defensible in customer and audit contexts

Technical Leadership & Standards

Establish and enforce analytics standards across:

SQL
Python
Data models
Power BI / Fabric artefacts

Mentor senior and mid-level analysts through technical review and solution design guidance.
Contribute to Analytics COE standards, patterns, and best practices.

Other Responsibilities

Develop, maintain, and optimize SQL-based datasets across Oracle and SQL Server environments, ensuring data quality, consistency, and reliability.
Perform complex data wrangling, cleansing, modelling, and transformation required for operational analytics, KPI reporting, and performance insights.
Build and maintain analytical datasets and scripts using Python to support automation, predictions, and advanced analysis.
Work with large and complex datasets from multiple operational systems (ServiceNow, device telemetry, transaction data, ODS/EDW).
Develop and maintain reports, dashboards, and data models in Power BI, including DAX, scheduled refresh, and governance-aligned practices.
Apply data science and ML concepts (e.g., classification, regression, clustering, feature engineering, model evaluation) to support predictive maintenance, anomaly detection, and optimization initiatives.
Collaborate closely with cross-functional teams to understand requirements, translate them into analytical solutions, and deliver insights that inform decisions.

Required Skills And Qualifications

Bachelor's/master's degree in Comp Science, Data Analytics, Engineering, Mathematics, or related field.
10 + years of experience in data analysis, analytics engineering, data solutions, or data science roles.
Expert-level SQL skills in both Oracle and SQL Server
Strong experience with Python
Proven experience designing analytical data solutions, not just consuming them.
Demonstrated experience partnering with engineering teams on data architecture and delivery.
Strong background in RCA, impact analysis, and operational performance analytics.
Deep understanding of:

KPI/SLA frameworks
Contractual measurement
Operational analytics in production environments
Data transformation and modelling

Proven ability to work with very large datasets, multi-source systems, and complex business logic.
Experience working with KPI/SLA-driven environments, producing measurement logic and detailed performance analysis.
Working knowledge of Power BI (modeling, DAX, optimisation, power query etc.)

Preferred Skills

Hands-on experience with Azure (ADF, SQL, Databricks, Data Lake) or other cloud data platforms.
Experience working in a transportation, operations, SaaS, or managed services environment.
Exposure to predictive maintenance, device health analytics, or operational reliability analytics.
Familiarity with version control (Git), DevOps, CI/CD data workflows.
Experience with data visualization best practices and storytelling techniques.
Knowledge of ServiceNow data models (incidents, requests, tasks, changes) is an advantage.

Soft Skills

Strong analytical mindset with exceptional problem-solving abilities.
Ability to work independently and take ownership of deliverables.
Excellent communication skills-capable of simplifying complex concepts for non-technical stakeholders.
Comfortable working in a fast-paced, global, multi-time zone team.
High attention to detail, data quality, and accuracy.
Strong sense of accountability, adaptability, and continuous improvement.
Ability to engage effectively with engineering, operations, and business stakeholders.
Proactive, resourceful, and committed to delivering high-quality outcomes.

Role Impact & Success Measures

The Principal Data Analyst will directly support CTS's global analytics capability by delivering high-quality insights, strengthening KPI/SLA measurement, and enabling data-driven decision-making across major transit programs. Success in this role is defined by strong analytical delivery, reliable data models, and meaningful contributions to operational and predictive initiatives.

Success in the First 36 Months

Build a solid understanding of CTS datasets, KPI frameworks, and key operational systems.
Support program teams with timely analysis that reduces manual reporting effort.
Establish credibility as the data SME across key programs.
Lead at least one major RCA or impact analysis end-to-end.
Influence data design decisions with Engineering teams.

Success in 612 Months

Take ownership of end-to-end analytical workstreams with minimal supervision.
Improve reporting efficiency by identifying and automating repetitive tasks.
Build strong relationships with program and global stakeholders.
Improve monitoring coverage and early-warning detection for operational risks.
Reduce RCA turnaround time through reusable frameworks.
Become a trusted escalation point for complex data questions.

Long-Term Success (12+ Months)

Contribute to CTS Analytics strategy, standards, and global operating model improvements.
Be recognized as a principal-level authority on CTS data and metrics.
Shape the evolution of the analytics platform and KPI strategy.
Influence how Services and Engineering design data-enabled solutions.
Uplift analytics maturity across the group through standards and mentoring.

Worker Type:

Employee