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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Engineering Manager (Data Platform) - **Company:** Preply Inc. - **Location:** London, UK - **Experience:** Expert - **Salary:** £102,564.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Business Logic, Code Generation, Software Quality, Information Engineering, Data Governance, Data Infrastructure, Data Warehousing, DevOps, Machine Learning, Azure Machine Learning, Feature Store, Retrieval-Augmented Generation, Apache Spark, Agentic-AI, Debezium, Apache Flink, Apache Kafka, Data Management, Data Pipelines - **Published:** October 11, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5923009058 ## About the Role * 8+ years of software or data engineering experience, including 5+ years in engineering management, with experience leading data or platform engineering teams. * People-centric approach and team-building skills: a passion for building high-performing teams and helping people grow. You know how to create alignment, foster psychological safety, provide actionable feedback, and develop engineers through coaching and mentorship. * Strong technical judgment and systems thinking. You are comfortable driving architectural discussions, evaluating technical trade-offs, championing engineering best practices, and partnering with senior engineers on technical strategy. * A track record of hiring, coaching, and retaining high-performing teams, including senior engineers. * Product & Business Partnership: You're comfortable working closely with Analytics, Data Science, ML, and Product partners to identify opportunities, prioritize investments, and drive measurable outcomes. You naturally connect data platform decisions to downstream user experience, business metrics, and company strategy. * Clear communication and stakeholder leadership, with the ability to align teams with different incentives, make sound decisions with incomplete information, and revisit assumptions as the data ecosystem changes. * Results-Oriented: A track record of driving performance. You know how to motivate teams to set and achieve ambitious, business-focused, and measurable goals. * Ethical Leadership: You lead by example, serving as the team's moral compass by demonstrating empathy, transparency, accountability, and a strong sense of ownership. You value honesty and fairness, creating a culture where people feel safe to challenge ideas, learn, and grow. * Hands-on familiarity with the modern data stack - for example, Spark, Flink, Kafka, Debezium, Airflow, dbt, or similar - and with cloud platforms (AWS/GCP or equivalent) and modern DevOps practices. * Experience leading teams that built data platforms used by multiple teams in fast-growing companies. * Experience running a data platform as a product: data contracts, self-service onboarding, discoverability, and measuring success through adoption and consumer outcomes. * Experience with data governance, privacy, and compliance at ingestion time (classification, access control, masking, auditability). * Familiarity with feature stores, ML data pipelines, or preparing data for GenAI / RAG use cases. * AI Expertise: You use Agentic AI beyond code generation to support architecture, planning, and decision-making, while retaining human judgment for architecture, people decisions, business logic, and security, and establishing quality gates and safety nets for team workflows. ## Description Learn more about how we build on our Engineering Blog, Tech Radar, and YouTube channel! * Lead and grow a high-performing data engineering team, fostering a culture of ownership, continuous improvement, and technical excellence. * Coach and develop engineers, support them through regular feedback, mentorship, and career development, while creating an inclusive and collaborative team environment. * Define and drive the long-term vision for Preply's data foundation - the Lakehouse, ingestion layers, data contracts, enrichment , and governance - ensuring alignment with company objectives and the needs of analytics, ML, and product teams. * Identify and prioritize high-impact opportunities, partner closely with Analytics, ML Platform, Data Science, and Product to ensure the team focuses on the datasets, pipelines, and tooling that deliver the greatest customer and business value. * Own execution and delivery. Translate strategy into realistic plans, balancing foundational platform investment with near-term needs of consuming teams while surfacing trade-offs and maintaining speed, quality, cost efficiency, and predictability. * Provide technical leadership, guide architecture across batch and streaming ingestion, data modeling, quality, and governance; help the team navigate trade-offs and remove technical obstacles. * Own data reliability and trust. Establish SLOs, observability, and incident response for data pipelines, moving the platform from reactive firefighting to proactive reliability management. * Champion engineering best practices. Drive continuous improvements in code quality, operational excellence, development processes, and technology evolution. * Collaborate cross-functionally, work with stakeholders across the broader business to maximize impact and align on priorities, data ownership, standards, and outcomes. * Own hiring quality, team composition, and operating model, evolving team boundaries as the platform and company needs change.