The purpose of the Senior Analytics Engineer role is to build and model data into robust, integrated and efficient data products to support and deliver best-in-class use case led analytics across the organisation. The role works collaboratively within and across a multi-functional agile team, building data products and pipelines for high impact projects that delivers scale and automation and improves data availability and quality. The Senior Analytics Engineer is a highly analytical and conceptual individual capable of engaging in the granular detail as well as the nuances of various systems to ensure data models are fully understood and built to deliver business value and drive data as a competitive advantage.
- Build data and feature marts for consumption by the data analyst, scientist, and machine learning communities.
- Set up monitoring, testing and automation procedures of data artifacts.
- Work with business stakeholders to understand business requirements and analyse and translate these into fit-for-purpose, robust and scalable data solutions.
- Organise and transform data in a meaningful way and provide additional context as necessary so that it is ready for analysis.
- Execute complex testing procedures and monitoring of datasets.
- Maintain documentation related to datasets and analysis, ensuring that everyone on the data team uses the same language and definitions.
- Productionisation of data products.
- Provide firstline data pipeline support, and contribute to overseeing data integrity and quality in pipeline.
- Consult with data analysts and data scientists to refine scripts and procedures to improve and maintain standard guidelines of the data pipeline process.
- Explore and discover opportunities to improve systems, enterprises, and processes.
- Collaborate across teams to address and improve data quality at the source.
- Foster continuous improvement by coaching adoption of software engineering best practices.
- Provide data modelling support to various teams through code reviews and training.
- Understand and apply best practises for own work while supporting the team to improve delivery standards and adopt best practices.
Qualifications and experience
- Degree or Diploma in a Computer Science, Engineering, Mathematics or a related field – (essential).
- +3 years’
experience working as part of a data team, ideally as a Data Scientist or Data Engineer with exposure to the productionisation of data science models / building and optimising data pipelines, architectures and data sets or similar – (essential).
- Strong knowledge of SQL with the ability to write SQL that is easy to understand, support and maintain – (essential).
- Proficiency working with large data sets and business models – (essential).
- Proficiency in AWS cloud computing-based Python / PySpark - (beneficial).
- Exposure to and comfortable with adopting software engineering best practices including version control – (preferred).
- Exposure to and at ease teaching others how to adopt best practices – (preferred).
- Experience building and maintaining multi-functional relationships with various teams across the business – (essential).
- Experience working in an Agile environment – (preferred).
- Retail or eCommerce industry experience – (preferred).
Key competences and work ethic
- A data specialist with the skill of practicing the art of data engineering. Solves complex data problems using their experience in scientific disciplines.
- Has strong analytical skills – Strong ability to collect, organise and assimilate disparate and multiple pieces of data to arrive at optimal solutions and embed the right structures and foundations
- Strong technical aptitude with a passion and excitement for data, new technologies and solutions and its range of possibilities, applications and value for the business.
- High level of self-motivation and drive to set, meet and exceed on goals and expectations. Uses own initiative in dealing with challenges as and when they arise.
- Detailed, organised and quality focused – Has an affinity for structure and efficiency and balances planning and execution. Is diligent and vigilantly watches over work processes, tasks and outputs to ensure accuracy while promptly initiating action to correct any quality concerns.
- Good communication skills – Communicates well both verbally and in writing. Able to simply more complex technical concepts and confidently convey information to a variety of stakeholders.
- Team player
and collaborative partner - Builds sound working relationships across the business. Able to work independently or collaboratively. Willing to coach / mentor others to build capacity and skills for data/analytics.
- Ability to work under pressure and under tight time constraints, efficiently prioritising workloads and managing their time effectively in a high-volume, fast-moving environment.
- Is curious and open to learning with a strong interest in data and discovery. Curious about exploring and answering business analytics questions.