Senior Data Platform Engineering Leader (Hybrid, London)

Preply; Greater London; Hybrid; GBP 140,000 - 190,000; Full time

Benefits offered by this job

Lessons allowance

Learning & Development budget

Health insurance

Gym access in London

Equity

Job summary

Preply in London is seeking a Senior Engineering Manager for Data Platform to own the engineering strategy and delivery for the company’s data foundation, lakehouse, and pipelines. This leadership role partners with Analytics, ML, Data Science, and Product teams to drive scalable, reliable data assets.

You will coach senior engineers, set standards, and balance long-term platform investments with near-term needs, while guiding data quality, governance, and observability across batch and

Qualifications

• Experience leading data or platform engineering teams
• Strong leadership and people-management skills
• Able to align architecture with business goals
• Excellent communication with stakeholders
• Experience with AI/ML data workflows is a plus

Responsibilities

• Own engineering strategy and execution for the Data Platform
• Lead and grow a high-performing data engineering team
• Collaborate with Analytics, ML, Product, Security
• Define data contracts, governance, and observability
• Drive reliability, SLOs, and proactive incident management
• Partner with cross-functional teams to prioritize impact
• Hire, develop, and retain senior engineers

Skills

8+ years engineering

Engineering management

People-centric leadership

Technical judgment

Hiring and mentoring

Cross-functional partnership

Communication

Results-driven

Ethical leadership

Tools

Spark

Flink

Kafka

Debezium

Airflow

dbt

Job description

Preply in London is seeking a Senior Engineering Manager for Data Platform to own the engineering strategy and delivery for the company’s data foundation, lakehouse, and pipelines. This leadership role partners with Analytics, ML, Data Science, and Product teams to drive scalable, reliable data assets.

You will coach senior engineers, set standards, and balance long-term platform investments with near-term needs, while guiding data quality, governance, and observability across batch and