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Preply

Online language-learning marketplace connecting live tutors

Senior Engineering Manager - Data Platform

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeLondon, UK

About the Job

Requirements

  • At least 8 years of software or data engineering experience, including at least 5 years in engineering management and experience leading data or platform engineering teams.
  • A people-centric approach and team-building skills, including the ability to build high-performing teams, create alignment, foster psychological safety, provide actionable feedback, and develop engineers through coaching and mentorship.
  • Strong technical judgment and systems thinking, including the ability to drive architectural discussions, evaluate technical trade-offs, champion engineering best practices, and partner with senior engineers on technical strategy.
  • A track record of hiring, coaching, and retaining high-performing teams, including senior engineers.
  • Ability to work closely with Analytics, Data Science, ML, and Product partners to identify opportunities, prioritize investments, and drive measurable outcomes, connecting data platform decisions to downstream user experience, business metrics, and company strategy.
  • Clear communication and stakeholder leadership, including the ability to align teams with different incentives, make sound decisions with incomplete information, and revisit assumptions as the data ecosystem changes.
  • A track record of driving performance and motivating teams to set and achieve ambitious, business-focused, measurable goals.
  • Ethical leadership demonstrated through empathy, transparency, accountability, and ownership, and the ability to foster a culture where people can challenge ideas, learn, and grow.

Responsibilities

  • Lead and grow a high-performing data engineering team, fostering ownership, continuous improvement, and technical excellence.
  • Coach and develop engineers through regular feedback, mentorship, and career development, while creating an inclusive and collaborative team environment.
  • Define and drive the long-term vision for the data foundation, including the Lakehouse, ingestion layers, data contracts, enrichment, and governance, aligning it with company objectives and the needs of analytics, ML, and product teams.
  • Identify and prioritize high-impact opportunities, partnering with Analytics, ML Platform, Data Science, and Product to focus the team on datasets, pipelines, and tooling that deliver customer and business value.
  • Own execution and delivery by translating strategy into realistic plans, balancing foundational platform investment with consuming teams’ near-term needs, surfacing trade-offs, and maintaining speed, quality, cost efficiency, and predictability.
  • Provide technical leadership and 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 by establishing SLOs, observability, and incident response for data pipelines, moving the platform from reactive firefighting to proactive reliability management.
  • Champion engineering best practices by driving continuous improvements in code quality, operational excellence, development processes, and technology evolution.
  • Collaborate with stakeholders across the business to maximize impact and align priorities, data ownership, standards, and outcomes.
  • Own hiring quality, team composition, and the operating model, evolving team boundaries as the platform and company needs change.

Desired Qualifications

  • Hands-on familiarity with the modern data stack, such as Spark, Flink, Kafka, Debezium, Airflow, dbt, or similar, and with cloud platforms such as AWS or GCP 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, including data contracts, self-service onboarding, discoverability, and measuring success through adoption and consumer outcomes.
  • Experience with data governance, privacy, and compliance at ingestion time, including classification, access control, masking, and auditability.
  • Familiarity with feature stores, ML data pipelines, or preparing data for GenAI or RAG use cases.
  • Use of 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.

About the Company

Preply

Online language-learning marketplace connecting live tutors

Preply is an online language-learning marketplace that connects learners with live tutors for 90+ languages across 180 countries. Through the platform, students can choose a language, browse tutors, schedule lessons, and receive video-based instruction from tutors around the world. Lessons are supported by an AI co-pilot toolkit that helps with practice, personalization, and learning data to aid the tutoring process. What sets Preply apart is its large, global network of more than 100,000 tutors, a broad language catalog, and a focus on combining human teaching with AI-assisted tools, backed by strong funding and profitability signals (it became EBITDA-positive) and a recent €127 million Series D to expand AI/data capabilities, product and engineering hiring, and global growth. The company’s goal is to make language learning accessible and effective for learners anywhere by scaling high-quality tutoring worldwide and enhancing it with AI-driven tools.

Company size
10,001+
Company stage
Series D
Total funding
$321.9M
Headquarters
Brookline, MA
Founded
2012

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