Key skills
Data EngineeringELT Stackdbtdbt Macrosdbt Testsdbt Exposuresdbt Semantic ModelsSnowflakeSnowflake Schema DesignSnowflake ClusteringSnowflake RolesPermissionsSnowflake Performance TuningAirbyteELT Ingestion ToolsDimensional ModelingKimball MethodologySemantic Layer ModelingMedallion ArchitectureELTAirflowFivetranSaaS
About this role
Harnham is a B2B SaaS company with approximately 700 employees and $120M in ARR. They are seeking a Senior Data Engineer to take end-to-end ownership of their dimensional and semantic data models, serving as the technical authority on transforming raw data into trusted business-ready information.
Responsibilities:
- Designing, building, and maintaining the medallion architecture (Bronze, Silver, Gold) using dbt, Snowflake, and Airbyte
- Owning the dimensional and semantic model end to end, from source alignment through to consumption-ready datasets
- Defining and enforcing data modeling best practices, naming conventions, and documentation standards across the stack
- Managing and optimizing Airbyte pipelines for data ingestion from SaaS and internal sources into Snowflake
- Partnering with analysts, data scientists, and business stakeholders to translate domain logic into reliable, well-structured models
- Improving query performance and Snowflake warehouse efficiency through thoughtful schema design and compute management
- Contributing to data quality frameworks, testing strategies, and observability tooling
Requirements:
- 5+ years of data engineering experience, with at least 2+ years working in a modern ELT stack
- Deep hands-on experience with dbt (Core or Cloud), including advanced features like macros, tests, exposures, and semantic models
- Strong Snowflake expertise including schema design, clustering, roles/permissions, and performance tuning
- Experience with Airbyte or similar ELT ingestion tools such as Fivetran or Stitch
- Solid understanding of dimensional modeling (Kimball methodology preferred) and experience building semantic or metric layers
- Comfort working within a medallion or layered data warehouse architecture
- Experience with a semantic layer tool such as dbt Semantic Layer / MetricFlow, Cube, or LookML
- Familiarity with orchestration tools like Airflow, Dagster, or Prefect
- Background in B2B SaaS metrics including ARR, churn, expansion, and product usage
- Experience with data observability tooling such as Monte Carlo or Elementary