Insights Product Manager, The Economist

• Analytics Engineering Mastery: Deep technical expertise in modern analytics engineering workflows (e.g., SQL, Python, dbt, Snowflake, or BigQuery) and data modelling
• Technical Business Analysis: Proven track record in gathering complex technical requirements and translating them into scalable data solutions
• People Leadership: Experience managing or supervising data/engineering professionals (Engineers or BAs) and a history of leading highly-motivated, high-performance teams, of setting and raising high standards and of identifying and nurturing talent
• Focus on Pace: The success of the Analytics Engineering function is fundamentally dependent on the pace at which it can deliver re-usable and robust data assets that strike a trade-off between the rigor/scalability of Data Engineering and the pace/flexibility of manual analytics
• Culture: Demonstrable track record of nurturing and training talent and of creating a culture of excellence, ownership, agency, learning and innovation
• Software Engineering Basics: Familiarity with software engineering principles (e.g., version control, CI/CD) to support the blurring lines between data and application development
• Modern Data Stack: Practical recent experience with tools such as Snowflake, Amplitude, Monte Carlo, or Google Analytics
• Change Management: Experience working through organizational re-designs or function-wide transformations
• Innovation with Impact: Track record of technical and process innovation that delivers impact not just POCs and of building teams and ecosystems that can do the same

Desirable

• As part of a goal to make the Data, Research & Insight (DRI) function fit for an AI-powered future, the Analytics Engineering Manager will be the primary lead for data enablement within the Insight Products team
• The core purpose is to ensure that high-quality, well-governed data is available for use by the Insight Products team and their stakeholders
• This role bridges the gap between raw data infrastructure and end-user insight products
• You will facilitate the radical democratisation of data by delivering high-quality requirements to the central Data Engineering team while leading your own team in undertaking "lighter" data engineering activities to accelerate delivery
• You will be a key technical resource for the creation of next-gen "Insight Products," including AI-powered analytics agents and custom applications
• Direct Reports: 3x Technical Business Analysts, 2x Analytics Engineers
• Qualitative:
• Data Trust: Significantly improved organizational confidence in data through superior governance, observability, and documentation
• Seamless Collaboration: Success is defined by an effective working relationship with the Business Intelligence (BI) Manager and central Data Engineering to maintain a robust data supply chain
• Community Support: Effectiveness in providing the technical foundation that allows decentralised users to successfully self-serve
• Quantitative Measures:
• Delivery Velocity: Demonstrable reduction in time-to-market for new data products, super-charged by AI-assisted development and "design-by-building" methodologies
• Data Reliability: Consistently meeting or exceeding defined SLAs for data quality, availability, and documentation
• Self-Serve Ratio: Increase in the ratio of automated self-serve usage compared to bespoke manual data requests
• Observability Metrics: Achievement of targets for data anomaly alerting and resolution times
• Team Leadership: You'll lead and mentor a team of Technical BAs and Analytics Engineers, fostering a culture of excellence, "full-stack" mindsets, and AI-powered efficiency
• Requirement Delivery: Working with the Insight Products Director and stakeholders to prioritise and define high-quality requirements for the central Data Engineering team
• Data Engineering & Operations: Oversee and execute internal data engineering tasks, including building data pipelines, tables, views, instrumentation, and tagging
• Technical Resource for BI: Act as a technical consultant and partner to the BI Manager in the development of conversational interfaces (Analytics Agents), custom UIs, and analytics apps
• Governance & Observability: Establish and maintain rigorous data documentation, catalogs, and observability processes (e.g., anomaly alerting) to ensure data is discoverable and reliable
• Data Enhancement: Lead efforts in third-party data enhancement and the curation of unstructured data to enable broader self-serve analytics