Data Analytics Engineer

Description:

As a Data Analytics Engineer in our team, you will ensure the data is accessible across the entirety of Gymshark to support all subsequent analytics and reporting.

Responsibilities:

• Providing clean, accessible data sources across Gymshark, to enable functions to answer key business questions and better understand business performance and/or customer behaviour, which in turn leads to making strategic decisions based upon our data.
• Managing data updates, imports, exports, segments, and audiences within analytical systems such as our insights database and marketing automation platform.
• Identifying problems with data quality, access, and processing, working across multiple systems and with various teams to identify the root cause, implement appropriate solutions through to resolution.
• Building, monitoring, and maintaining data process, documentation and reporting that improve our data assets (cleansing, quality, enrichment etc.)
• Working with third party partners and software providers to improve and/or implement new data solutions and processes.
• Being the gatekeeper for customer data, aware of our usage and alignment to data privacy standards, in collaboration with Data Governance to ensure best practice standards are applied/maintained.

Qualifications:

• Strong experience with data analysis tools and technologies (e.g., SQL, Python, Snowflake, Excel, Alteryx, AWS)
• High class numerical, technical, and problem-solving skills
• Track record in translating business data issues with an effective data solution.
• Strong stakeholder management skills: working with both technical & non-technical stakeholders, acting as the translation/intermediary to simplify terminology as/when needed.
• Need to be a methodical, analytical, and logical thinker, able to see the bigger picture, and get to underlying business questions and challenges.
• Experience working with Customer Data Platforms (“CDP”) would be beneficial.
• Confident at working with in-house Systems teams.
• Able to separate BAU from core objectives, managing workloads effectively.
• Demonstrate delivery under pressure and to deadline.
• Awareness of Business Intelligence (“BI”) tools such as Tableau, Looker, Quicksight.