Remote Associate Data Engineering Manager
Role details
Job location
Tech stack
Job description
As an Associate Data Engineering Manager for the newly formed Market Data team at RoomPriceGenie, you will have a unique opportunity to build and shape a foundational capability within the company.
This team is being created to centralise and professionalise how we acquire, structure and scale external data sources across the organisation. What started as distributed side initiatives across multiple teams will now become a dedicated, scalable and best-practice-driven data platform.
This is a hands-on leadership role, ideal for someone who has had some experience managing engineers and is eager to continue growing their management craft. You'll continue to be technical (spending some of your time coding, reviewing or pairing) while driving clarity, focus and technical excellence across the team.
You'll partner closely with Product and senior engineering leadership to define the roadmap, establish strong technical foundations and create a high-performing, empowered team culture., * Build, lead and develop a team of backend and data engineers.
- Run regular 1:1s, provide feedback and actively mentor engineers in both technical and professional growth.
- Create a strong team identity, ownership mindset and high-performance culture from day one.
- Hire and onboard new engineers as the team scales.
- Drive the design of scalable, maintainable and cost-efficient data acquisition and transformation systems.
- Establish strong engineering best practices in system design, testing, observability and documentation.
- Improve and modernise existing data workflows by turning ad-hoc initiatives into structured, production-grade systems.
- Ensure the team builds with long-term scalability and reliability in mind.
- Act as a reference point for data engineering best practices across all teams.
- Collaborate closely with other Engineering Managers to ensure alignment and shared standards.
- Contribute to architectural decisions that impact company-wide data strategy.
- Promote a culture of data quality, clear ownership and measurable outcomes.
- Stay technically involved through code reviews, architectural discussions and occasional coding.
- Support engineers in solving complex system design challenges.
Requirements
Do you have experience in System design?, * Experience leading engineers in a technical or team lead capacity.
- Strong coaching mindset, you enjoy growing people, not just systems.
- Comfortable building structure in a new or evolving environment.
- High ownership mentality with the ability to balance autonomy and accountability.
- Solid experience as a Software or Data Engineer, with production system ownership.
- Strong experience in Python (4+ years).
- Experience building and maintaining production-grade ETL/ELT pipelines in modern cloud data warehouses (e.g. Snowflake, BigQuery, Databricks, Redshift).
- Strong data modeling skills (analytics-ready schemas, performance optimisation, testing and documentation practices).
- Experience with orchestrated data pipelines (e.g. Dagster, Airflow or similar).
- Strong system design and architecture skills with a focus on scalability and maintainability.
- Analytical and structured thinker with a strong focus on long-term architecture.
- Comfortable operating in ambiguity and turning it into clarity.
- Pragmatic but quality-driven.
- Collaborative and low-ego, you build trust across teams.
- Fluent in English and based in the European time zone (UTC+0 / UTC+2).
Nice to Have
- Experience running regular 1:1s and supporting performance conversations.
- Previous formal people management experience.
- Experience building or restructuring data platforms from scratch.
- Experience scaling engineering teams in a high-growth environment.
- Hands-on experience with dbt (modeling, testing, documentation).
- Experience modernising legacy data pipelines.
- Familiarity with Django or FastAPI ecosystems.
- Experience with observability and monitoring tools (e.g. Datadog, Sentry).
- Experience working with distributed task queues (e.g. Celery & RabbitMQ).
- Strong understanding of cost-aware architecture in data-intensive systems.