Groov is a Workplace Science & Analytics platform that delivers actionable insights in the flow of work to make workplaces better. We apply science to workplace data, generating tailored insights for individuals, managers, and leaders that improve productivity, morale, and job satisfaction.
Our multidisciplinary team combines expertise in workplace psychology, data engineering, AI engineering, and software development to transform how organisations operate.
We partner with enterprises to solve their workplace challenges using large dynamic data sets of passive and active data, leveraging AI and grounding our solutions in cutting edge workplace science. Our approach enables organisations to test, learn, and optimize how leaders lead and how teams work with real-time insights that improve both performance and employee care.
Role Overview
We're seeking a talented Intermediate Data Engineer with a focus on feature engineering and supporting workplace scientists. In this role, you'll report to our Senior Data Engineer and work remotely as part of our cross-functional product development team to transform workplace data into meaningful, actionable insights.
You'll create data features for our analytical models, support the development and maintenance of data pipelines, and collaborate with workplace scientists to translate research questions into technical solutions. This position offers the opportunity to directly impact how organizations understand and improve their workplace environments through data-driven insights.
Key Responsibilities
- Feature Engineering
- Transform modelled data into meaningful features for analysis
- Work with workplace scientists to understand analytical needs and create appropriate data features
- Implement statistical transformations and validations to ensure feature quality
- Document feature definitions, transformations, and lineage
- Cross-Team Collaboration
- Partner closely with workplace scientists to translate research questions into data solutions
- Work with product and engineering teams to integrate data solutions into the platform
- Communicate technical concepts and limitations to non-technical stakeholders
- Data Quality & Governance
- Implement data validation frameworks and quality checks
- Establish and maintain proper data documentation and metadata
- Support data lineage tracking and version control for features and datasets
- Help define and enforce data standards and best practices
- Technical Operations
- Debug data pipeline issues and resolve data quality problems
- Contribute to monitoring systems for data flows and feature creation
- Write tests for data transformations and feature engineering code
- Support continuous improvement of data engineering practices
- Data Pipeline Development
- Maintain and enhance existing data processing pipelines
- Support ETL processes that integrate data from various workplace tools and systems
- Ensure data quality, consistency, and reliability across all data flows
- Assist in optimizing data transformation processes for performance and scalability
Required Skills
- Advanced Data Processing & Feature Engineering
- 3+ years experience implementing data engineering for analytical purposes
- Ability to understand and evaluate trade-offs in data transformation approaches
- Experience preparing workplace/HR data for analysis
- Proficiency in creating meaningful, actionable features from modelled datasets
- Data Analysis Capabilities
- Strong analytical skills with experience in identifying data quality issues, outliers, and patterns through exploratory data analysis
- Ability to conduct systematic data exploration using statistical methods and data mining techniques
- Experience with data visualization tools (Power BI, Tableau, or similar)
- Statistical knowledge to properly transform and validate data features
- Proficiency with analytical tools and techniques
- Collaboration Excellence
- Strong skills working directly with workplace researchers
- Ability to translate research questions and analytical needs into appropriate data features
- Skills in communicating technical concepts to non-technical team members
- Experience in iterative development based on researcher feedback
- Commitment to supporting scientific discovery through robust data engineering
- Strong Programming Fundamentals
- Proficient Python development (3+ years experience) with demonstrated ability to:
- Write well-structured, documented, and tested code
- Implement efficient algorithms and data structures
- Apply object-oriented and functional programming concepts appropriately
- Experience with data manipulation libraries (pandas, NumPy, scikit-learn)
- Understanding of software engineering principles (DRY, modularization, readability)
- Proficiency with version control, code reviews, and technical documentation
- Design of efficient, scalable data processing workflows
- Problem-Solving Abilities
- Critical thinking to identify the best technical approaches
- Ability to debug complex data issues
- Self-directed learning to expand technical capabilities as needed
Preferred Qualifications:
- Bachelor's degree in Computer Science, Data Engineering, or a related field (or equivalent practical experience)
- Knowledge of statistical methods and machine learning concepts
- Familiarity with business intelligence tools
- Experience with data governance and lineage tracking
- Understanding of software development lifecycle and CI/CD practices
Salary Range
$100,000 - $130,000 NZD annually, depending on experience and qualifications. In addition to your base salary, we offer equity through our Employee Share Option Plan.
