Insights Product Manager - Analytics Engineering

Who we are

Since 1843, The Economist Group has championed independence, excellence and openness, helping people understand and tackle the critical challenges shaping the world. Today, we are building on that legacy as a global media and information-services company powered by digital innovation, analytical rigour and evidence-based insight.

Across our three businesses -The Economist, Economist Enterprise and Economist Education - we deliver trusted analysis and insights to individuals and organisations in more than 170 countries. United by a shared purpose to drive progress, we empower decision-makers to make sense of change and chart a course through an increasingly complex world.

As a colleague, you will be part of a culture that values ideas, encourages ownership and holds itself to high standards. We invest in people who are curious, thoughtful and adaptable, whether they are launching new products, reporting on global events or harnessing emerging technologies such as AI to improve how we work. Here, fresh thinking is taken seriously, ambition is matched by integrity, and great work is recognised. Working across disciplines, geographies and perspectives, we are united by a commitment to innovation, excellence and creating meaningful impact.

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 

Measures of Success

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.

Responsibilities

• Team Leadership: 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.

Who you are

• 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 

• Emerging Tech (AI/LLM): Experience building or deploying AI-powered agents, conversational interfaces, or leveraging LLMs for data discovery.
• Data Governance: Hands-on experience with data documentation, quality monitoring tools, and establishing data catalogues.

#LI-Hybrid 

Working Arrangements

The majority of our roles operate on a hybrid working pattern, with 3+ days office attendance required. 

AI usage for your application

We are an innovative organisation that encourages the use of technology. We recognise that candidates may utilise AI tools to support with their job application process. However, it is essential that all information you provide truthfully and accurately reflects your own experience, skills, and qualifications.

What we offer

Our benefits package is designed to support your wellbeing, growth, and work-life balance. It includes a highly competitive pension or 401(k) plan, private health insurance, and 24/7 access to counselling and wellbeing resources through our Employee Assistance Program.

We also offer a range of lifestyle benefits, including our Work From Anywhere program, which allows you to work from any location where you have the legal right to do so for up to 25 days per year. In addition, we provide generous annual and parental leave, as well as dedicated days off for volunteering and even for moving home.

You will also be given free access to all The Economist content, including an online subscription, our range of apps, podcasts and more.