Data Engineer

About the Role

As a Data Engineer at Obsidian, you'll:

• Own and evolve the data pipelines behind one of Obsidian's core products
• Build and maintain data transformations and orchestrated jobs that turn raw signal into attributed, enriched data at scale
• Keep customer analytics datasets accurate, fresh, and reliable across multiple data stores
• Extend and improve our data attribution engine, including its rule-based and LLM-assisted curation stages
• Make occasional changes to the Go service that exposes this data through our APIs, working alongside backend engineers
• Collaborate with product managers, backend engineers, and the teams who turn this data into customer-facing functionality
• Champion data correctness, observability, and clean, testable transformations
• Participate in code reviews and technical discussions to continuously raise engineering standards

What's in it for you

• Own the data foundations of a core product used by enterprises worldwide
• Work alongside a talented, supportive team in a collaborative culture
• Grow your skills across modern data tooling, streaming systems, and LLM-assisted workflows
• Gain exposure to a cloud-agnostic platform spanning AWS and GCP
• Be part of an innovative, growing company where your work makes a real impact
• Enjoy hybrid working, with supported remote working and great office spaces in Cheltenham and Manchester

Required Skills and Experience:

• 3–6 years of experience in a data engineering or software engineering role
• Strong SQL and hands-on experience building production data pipelines
• Experience with a modern data orchestrator such as Dagster, Airflow, or similar
• Proficiency with a data transformation framework such as dbt
• Proficiency in Python
• Familiarity with Git and CI/CD tooling such as GitLab CI/CD
• Familiarity with relational databases (e.g., Postgres) and cloud data warehouses
• A bias toward data quality, testing, and maintainable, well-documented work
• Experience collaborating in a team environment and adapting to changing requirements

Desirable Experience

• Experience with Databricks/Spark or other large-scale analytics platforms
• Exposure to event/streaming systems such as Kafka
• Existing Go experience for occasional API changes
• Familiarity with containerization and orchestration (Docker, Kubernetes)
• Exposure to LLM-assisted data workflows
• Experience with cloud platforms (AWS or GCP) and object storage (S3/GCS)
• Exposure to observability tooling such as Grafana or Prometheus