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Jobs(26,670)/Data Engineer Role(602)/moneyboxapp (19) - Moneybox - Data Product & Analytics Engineering Senior Manager
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moneyboxapp - Moneybox - Data Product & Analytics Engineering Senior Manager

London - Hybrid+ Equity3w ago
In OfficeSeniorEMEAFintechData AnalyticsOil & GasData EngineerAnalytics EngineerSenior Data ScientistTeam ManagementSnowflakedbtDatabricksDocumentation

Requirements

• Someone comfortable building technical solutions as you are communicating them to non technical stakeholders • A strong previously hands-on Data Engineer and/or Analytics Engineer. • A clear communicator with experience translating business problems into scalable data products and technical requirements. • Excited about fintech and the particular challenges that come with a regulated, app-first, long-consideration financial product. • Comfortable with ambiguity and able to operate with autonomy in a fast-paced environment where the brief sometimes evolves mid-sprint. • 5+ years of experience with data architecture and analytical data modelling. • Experience with modern transformation frameworks such as dbt and cloud data platforms such as Databricks, Snowflake or equivalent. • Experience leading, mentoring or technically guiding Data/Analytics Engineers. • Understanding of the complete data lifecycle from source applications and ingestion through transformation, semantic modelling and consumption. • Experience managing roadmaps and prioritising competing business, architectural and technical requirements. • The ability to communicate comfortably with both engineers and senior business stakeholders. • If you meet most but not all of the above, we’d still encourage you to apply. • Knowledge of data quality, testing, lineage, documentation, ownership and governance practices. • Experience working in a regulated industry, ideally financial services or fintech, with an understanding of how governance, controls and regulatory requirements influence data architecture and delivery. • Experience with Databricks and dbt, particularly designing or evolving a centralised transformation/modelling layer. • Experience managing dependencies across Product Engineering, Data Engineering and Analytics teams, particularly where delivery requires changes to upstream applications or event/data generation. • Our Commitment to DE&I: • At Moneybox, we promote, support and celebrate inclusion, diversity and equity for all, so that everyone can bring their full selves to work. We believe that diversity drives innovation, and that if our team is representative of our community of customers, we can better support their needs. To ensure our recruitment processes provide an equal opportunity for all applicants to succeed, we encourage you to let us know if there are any adjustments that we can make. We are open-minded and always willing to go the extra mile to ensure all applicants can present their full self and potential • Working Policy: • We have a hybrid policy that includes 2 days from our London office and 3 from home. If the role states it is either hybrid or remote candidates must be based within the UK. • Visa Sponsorship: • At this time we cannot offer visa sponsorship for this role and we cannot consider overseas applications. • Please note if offered a position, the offer is conditional and subject to the receipt of satisfactory pre-employment checks which we will conduct such as criminal record and adverse credit history checks. As a regulated financial business, an adverse financial history could impact your suitability for the role. If you are aware of anything that could affect your suitability for the role, please let us know in advance. • By sending us your application you acknowledge and agree to Moneybox using your personal data as described below. • We collect applicants’ personal data to manage our recruitment related activities. Consequently, we may use your personal data to evaluate your application, to select and shortlist applicants, to set up and conduct interviews and tests, to evaluate and assess the results, and as is otherwise needed in the recruitment process generally.We do not share your personal data with unauthorised third parties. However, we may, if necessary, share your personal data to carefully selected third parties acting on our behalf. This may include transfers to servers and databases outside the country where you provided us with your personal data. Such transfers may include for example transfers and/or disclosures outside the European Economic Area and in the United States of America. • If you are unsuccessful in your application, we may keep your details on file so that we can tell you about other suitable vacancies which may be of interest to you when they arise in the future. • If you would like to reach us then please email: [email protected] • If you would rather we did not keep your details on file, you can contact us at: [email protected]

Responsibilities

• Lead the Analytics Engineering team • Manage and develop a team of four Analytics Engineers across different levels of seniority. • Provide technical direction, coaching and context while creating an environment where engineers retain ownership of solution design and implementation. • Work with the team on modelling and architecture decisions, challenge proposed approaches, identify opportunities for reuse and simplification, and ensure solutions align with our target data architecture. • Coach engineers at different levels, helping them improve not only their technical capabilities but also their understanding of business problems, architectural thinking and ownership of data products. • Own the Analytics Engineering roadmap. • Define and maintain a prioritised roadmap covering new data models, data marts, dimensions and attributes, governed metrics, and architectural improvements. • Drive the transition towards more centralised and trusted data models, reducing duplicated transformation logic and creating reusable representations of core business entities, facts, dimensions, metrics and relationships. • Own data products end-to-end. • Take requirements from problem definition through discovery, architecture, prioritisation, delivery, quality assurance, adoption and ongoing ownership. • Coordinate dependencies across Product Engineering, Data Engineering, Insights, Reporting and other teams. Ensure upstream changes and ingestion requirements are identified early enough to support downstream data delivery. • Understand the underlying problem behind stakeholder requests and determine whether the right solution is a new attribute, metric, model, mart or enhancement to an existing data product.

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