Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US.
As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations.
Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow.
Senior Data Engineer
We’re hiring a Senior Data Engineer to join Synthesia and take ownership of our core data systems. You’ll be responsible for designing and maintaining scalable pipelines, optimising data models, and ensuring high data quality and governance standards.
What you'll do at Synthesia:
Architect and scale robust, end-to-end data pipelines that ingest and transform complex semi-structured and structured data into our Snowflake data warehouse.
Own the evolution of our dbt project - implementing modular modelling patterns and other best practices to ensure a "single source of truth" for the entire organisation.
Manage platform infrastructure in snowflake, AWS and other tools.
Continuously optimise warehouse performance and cost by diagnosing bottlenecks, tuning inefficient queries, and improving how compute resources are used as we scale.
Bridge the gap between experimental data science workflows and production, building the infrastructure and orchestration needed to deploy and monitor batch ML jobs.
Drive best practices in data security, governance, and compliance, particularly with regards to AI.
Partner with cross-functional stakeholders to understand data requirements and translate them into technical solutions.
What we’re looking for:
5+ years of experience as a Data Engineer or in a closely related role, with a proven track record of building and operating production data systems.
Experience working in an early-stage or scaling data function. You’re comfortable taking ownership and wearing multiple hats when needed.
Strong foundations in software engineering and data modelling best practices, with an ability to design systems that are maintainable, scalable, and easy for others to build on.
Deep expertise in SQL, and solid experience using Python or similar languages to build data pipelines, tooling, and orchestration (Airflow).
Hands on experience managing cloud infrastructure using infrastructure-as-code (e.g. Terraform) on AWS, GCP, or similar platforms.
A pragmatic approach to data platform design, with an eye for performance, cost efficiency, and operational reliability.
Excellent communication skills: you can work effectively with technical and non-technical stakeholders to gather requirements, explain trade-offs and communicate data team needs.
A product-oriented mindset, with an understanding of how data can shape decision making and accelerate company growth.
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