Fleet Data Analyst

• Develop & Maintain Fleet Health Dashboards:

  Design, build, and maintain comprehensive dashboards and reporting tools using platforms like Grafana or Tableau to monitor key performance indicators (KPIs) for our entire device fleet.

• Perform Deep-Dive Analysis:

  Investigate anomalies, trends, and patterns in large-scale IoT data to identify root causes of hardware or software issues and predict potential failures.

• Collaborate Cross-Functionally:

  Partner closely with engineering and support teams to understand their challenges, define data requirements, and deliver analyses that answer critical questions about device behavior and reliability. It is also critical to work with software teams to supply reporting requirements that can help establish higher quality metrics across other platforms and homegrown tooling.

• Build Data Pipelines:

  Architect and implement scalable and reliable ETL/ELT pipelines to process, clean, and aggregate time-series data from our IoT devices whilst collaborating with business intelligence to standardize on data definitions.

• Translate Data into Action:

  Communicate complex findings and recommendations clearly and concisely to both technical and non-technical stakeholders, influencing product strategy and engineering roadmaps.

• Ensure Data Integrity:

  Act as a steward for monitoring of fleet data, ensuring its accuracy, completeness, and accessibility for analysis whilst flagging anomalies, gaps or concerns.

About You

• Bachelors degree in a quantitative field like Computer Science, Engineering, Statistics, or a related discipline.
• 5+ years of experience in a data analyst, data scientist, or data engineer role, with a proven track record of working with large, complex datasets.
• Strong proficiency in

  SQL

  and a scripting language like

  Python

  (including libraries such as Pandas, NumPy, and Matplotlib).
• Hands-on experience with data visualization tools (e.g.,

  Tableau, Grafana

  ).
• Solid understanding of data warehousing principles and experience working with cloud data platforms (e.g., AWS Redshift, Google BigQuery, Snowflake).
• An innate curiosity and a sharp analytical mind, with the ability to break down ambiguous problems into solvable components.

• Direct experience analyzing

  IoT, telemetry, or time-series data

  .
• Background in the renewable energy, smart home, or connected hardware industries.
• Experience building predictive models or applying statistical analysis to understand hardware reliability.
• Familiarity with data pipeline orchestration tools like Prefect.
• Knowledge of big data technologies such as Spark