Position Overview
Our client is seeking an experienced Metrics Design & Reporting Specialist – Splunk to transform complex operational and security datasets into clear, actionable, decision-enabling reporting. In this role, you will bridge technical data infrastructure with strategic business objectives by designing, building, and optimizing dashboards and reporting workflows for executive leadership, engineering, risk, and compliance teams.
Key Responsibilities
- Dashboard Design & Visualization: Design, build, and maintain high-impact dashboards and enterprise reports in Splunk and complementary visualization platforms to communicate key operational, risk, and compliance metrics.
- Data Engineering & Querying: Author, test, and optimize clean, maintainable query logic (SPL, Python, SQL) to extract actionable insights from large, multi-faceted datasets.
- Pipeline & Workflow Management: Utilize workflow orchestration tools (e.g., Apache Airflow, Prefect) to automate data ingestion, pipeline monitoring, and reporting schedules.
- Stakeholder Communication: Translate complex technical output into structured narrative presentations and key performance indicators tailored for diverse non-technical audiences, leadership, and regulatory bodies.
- Compliance & Risk Support: Map framework controls and compliance requirements into technical data metrics to support audit readiness and risk assessment functions.
Required Qualifications
- Technical Expertise: Hands-on experience with enterprise reporting and log analytics platforms (Splunk, ELK Stack, or Tableau).
- Programming Languages: Proficiency in SPL (Splunk Processing Language), along with solid experience in Python, Scala, or Java.
- Database & Data Modeling: Strong foundational knowledge of relational database design, SQL querying, optimization, and NoSQL architecture.
- Automation: Practical experience with workflow orchestration tools (Apache Airflow, Prefect, or equivalent) for data pipeline automation.
- Analytical Thinking: Demonstrated capacity to break down ambiguous technical problems into structured data solutions.
- Education: Bachelor’s degree in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative field (or equivalent practical experience).
Nice-to-Have Experience
- Industry Experience: Background in large enterprise environments, specifically within Financial Services or highly regulated sectors.
- Control Frameworks: Familiarity with cybersecurity and regulatory frameworks, such as OSFI B-13, NIST CSF 2.0, CIS Controls, or CSA.
- Audit & Risk: Prior exposure to internal audit processes, external regulatory examinations, or cloud security cost/scalability metrics.
