Senior Data Engineer
Summary
Senior Data Engineer responsible for building and maintaining data models, ingestion pipelines, and platform components. The role involves working with dbt, Snowflake, and Python to ensure data quality and reliability for internal teams and enterprise customers within the veterinary clinic engagement space.
About this role:
The company is expanding its data engineering capacity to keep pace with growth in its product portfolio and in analytical demand from internal teams and enterprise customers. These openings add senior engineering capability to the company's Sri Lanka-based group, where a small data engineering team is being formed alongside existing local staff. The Sr. Data Engineer builds and maintains the models, pipelines, and platform components that make company data trustworthy and usable, taking assigned workstreams from investigation through design, implementation, and support. This role suits an engineer comfortable digging into unfamiliar source data to work out what it represents, rather than waiting for a complete specification.
Apply if you’re excited to:
- Designs and builds dimensional and analytical models in dbt against a cloud data warehouse, across core domains and new business lines.
- Develops and maintains ingestion and orchestration pipelines in dbt Core, Snowflake SQL, and Python, keeping assigned pipelines running reliably on schedule.
- Profiles source data from transactional systems and third-party integrations to determine grain, keys, and business meaning, and documents the resulting assumptions.
- Implements data quality tests, monitoring, and alerting so anomalies are caught before they reach reports or customer-facing outputs.
- Diagnoses production data issues across ingestion, transformation, and source layers, documenting root cause for the wider team.
- Participates in design and code review with engineers across regions, giving and receiving substantive technical feedback.
- Partners with analysts and stakeholders to clarify requirements and validate results.
About you:
- 5+ years in data engineering, including 3+ years building analytical models in a cloud data warehouse.
- Advanced SQL: writing, debugging, and optimizing complex analytical queries over large datasets; Snowflake preferred.
- Production dbt experience: model layering, testing, documentation, incremental strategies, in a version-controlled project.
- Proficient in Python for pipeline development, API and file-based integrations, and orchestration with Airflow or equivalent.
- Demonstrated ability to investigate undocumented source systems and map data to real business processes, with judgment about what to model and what to flag.
- Familiarity with dimensional modeling and the trade-offs between normalized, wide, and aggregate structures.
- Git-based development, pull request review, and CI/CD pipelines.
- Enough exposure to BI tools (Tableau, Looker, Power BI, or similar) to model data that serves reporting well.
- Clear written and spoken English and strong async habits; thorough PR descriptions, written context for decisions.
- Conscientious about data accuracy; raises questions when the data doesn't look right.
PetDesk combines the industry's most user-friendly client engagement tools with the highest-rated pet parent app to simplify your veterinary clinic’s operations while strengthening client relationships.
Our recruiting process is rooted in “Who: The A Method of Hiring” and consists of an average 2-week hiring timeline. This standardized interview process allows candidates to answer the same questions, experience the same process, and ensure a fair performance review from multiple perspectives.
Please, no external recruiters—candidate profiles submitted from external recruiting agencies will not be considered.
Skills
As published by lever · 4 questions · 3 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Other URL
Pick from a list (1)
- Are you comfortable working EU hours?
Written answers (3)
- Describe one time a source table or field turned out to mean something different from what you or your stakeholders assumed. State what it was assumed to mean, what it actually meant, how you discovered the difference, and what it affected downstream. (Max 100 words)
- For one incremental dbt model you personally owned in production, state its grain, its unique key, the incremental strategy used, and how it handles late-arriving records. (Max 40 words)
- Describe the most recent production data issue you diagnosed yourself: what broke, which layer the root cause was in, and what specific test or alert you added afterward. (Max 50 words)