Description:

Groov is a Workplace Science & Analytics platform that provides actionable insights to improve workplace environments.
The company utilizes workplace data to enhance productivity, morale, and job satisfaction through a multidisciplinary approach.
The Intermediate Data Engineer will focus on feature engineering and support workplace scientists in transforming workplace data into actionable insights.
This role involves creating data features for analytical models, maintaining data pipelines, and collaborating with scientists to address research questions.
The position is remote and part of a cross-functional product development team, directly impacting how organizations understand and improve their workplace environments.

Requirements:

A minimum of 3 years of experience in data engineering for analytical purposes is required.
Candidates must have the ability to evaluate trade-offs in data transformation approaches and prepare workplace/HR data for analysis.
Strong analytical skills are necessary, including experience in identifying data quality issues and conducting exploratory data analysis.
Proficiency in data visualization tools such as Power BI or Tableau is required.
Candidates should have strong programming skills in Python, with at least 3 years of experience in writing well-structured and tested code.
Experience with data manipulation libraries like pandas, NumPy, and scikit-learn is essential.
A Bachelor's degree in Computer Science, Data Engineering, or a related field is preferred, along with knowledge of statistical methods and machine learning concepts.
Familiarity with business intelligence tools and data governance practices is advantageous.

The salary range for this position is $100,000 - $130,000 NZD annually, based on experience and qualifications.
In addition to the base salary, employees are offered equity through the Employee Share Option Plan.
The role provides an opportunity to work remotely and be part of a dynamic team focused on workplace science and analytics.