Senior Data Engineer

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.