Data Engineer

at  Groov

Groov is a company based in Auckland, New Zealand, on a mission to help millions of people feel and perform better at work. We do this by combining the best of psychology and behavioural science, data science, and user experience to deliver the right experience at the right time in the right way to assist employees, managers and leaders.
Groov’s technology lifts engagement and performance by prompting people to take appropriate action to care for themselves and others based on their roles, activities and personalities and is integrated into their flow of work. It also provides real-time insights to help managers understand individual and team engagement, focus and burnout risk, and provide them with recommendations on why, when and how to support individuals and teams.
Groov is looking for an experienced (ideally a Senior) Data Engineer to join our Data Science team. As a data engineer at Groov, you will be responsible for developing, maintaining, and optimizing our data warehouse, data pipeline, and data products. The data engineer will support multiple stakeholders, including data scientists, psychologists and scientists, and software developers, to ensure an optimal data architecture and delivery. The ideal candidate should possess strong technical abilities to solve complex problems with data, a willingness to learn new technologies and tools if necessary, and be comfortable supporting the data needs of multiple teams, stakeholders, and products. The position could be partially or fully remote.

EXPERIENCE

• Bachelor’s degree in Computer Science, Information Systems, or a related field.
• Five+ years of relevant working experience.

MINIMUM QUALIFICATIONS

• Advanced SQL skills and experience with relational databases and database design.
• Experience working with cloud Data Warehouse solutions (e.g., Snowflake, Redshift, BigQuery, Azure, etc.).
• Experience working with data ingestion tools such as Airbyte, Fivetran, Stitch, or Matillion.
• Working knowledge of Cloud-based solutions (e.g. AWS, Azure, GCP).
• Experience building and deploying machine learning models in production.
• Strong proficiency in one or more programming languages such as Python, PySpark, .Net, Node.js, Java, C++, Scala, and also scripting languages like Bash.
• Demonstrated ability in deploying machine learning models in production and ML Ops practices.
• Strong proficiency in data pipeline and workflow management tools (e.g., Airflow, Azkaban).
• Strong project management and organizational skills.
• Excellent problem-solving, communication, and organizational skills.
• Proven ability to work independently and collaboratively in a fast-paced environment.

ADDITIONAL DESIRABLE QUALIFICATIONS

• Experience with working on large data sets, distributed computing (e.g. Hive, Hadoop, Spark, Presto, MapReduce) and Machine Learning operations
• Experience with using software such as R or Python with Pandas, NumPy, etc. for machine learning and statistical analysis
• Experience with using Large Language Models such as evaluating, optimizing and fine-tuning them for Retrieval Augmented Generation use.
• Location in Auckland preferred; remote is possible

• Design, build and maintain batch and real-time data pipelines in production.
• Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading (ETL) of data from a wide variety of data sources.
• Develop ETL processes to extract and manipulate data from multiple sources.
• Automate data workflows such as data ingestion, aggregation, and ETL processing.
• Prepare raw data in Data Warehouses into consumable datasets for both technical and non-technical stakeholders.
• Partner with data scientists and functional leaders in sales, customer service, and product to deploy machine learning models in production.
• Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (especially AWS).
• Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures to meet ISO and GDPR standards.
• Monitor data systems performance and implement optimization strategies.
• Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership.