Snowflake Engineer
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Key skills for this role
Role Overview
The Senior Data Engineer will be the first dedicated internal data engineering hire in Lunate’s Data & AI function.
The role builds dependable Snowflake ingestion and transformation pipelines for reporting, operations, and AI-enabled products.
The role partners with the Head of Data & AI and Infrastructure to establish a controlled, repeatable data-as-code practice supporting BI and AI workloads.
Key Skills for This Role
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Firm Overview
Lunate is an Abu Dhabi-based, partner-led independent global alternative investment manager.
The firm has more than 250 employees and $115 billion of assets under management.
Lunate invests across private markets and public equities and credit.
Job Purpose
The Senior Data Engineer will be the first dedicated internal data engineering hire in Lunate’s Data & AI function.
The role builds dependable Snowflake ingestion and transformation pipelines for reporting, operations, and AI-enabled products.
The role partners with the Head of Data & AI and Infrastructure to establish a controlled, repeatable data-as-code practice supporting BI and AI workloads.
Key Duties and Responsibilities
- Build and own Snowflake ingestion pipelines using modern ELT patterns and custom ingestion where needed.
- Implement layered raw, standardised, and business data architecture with immutability, auditability, reprocessing, and backfill support.
- Develop SQL and Python transformations as version-controlled code using dbt or equivalent tooling.
- Apply Kimball or relational data modelling approaches where appropriate.
- Build automated data quality and reconciliation checks with visible quality signals.
- Operate pipelines with CI/CD, automated testing, peer review, and controlled promotion across environments.
- Optimise Snowflake usage for performance and cost with Infrastructure.
- Deliver AI-ready datasets with consistent semantics and clear lineage.
- Translate investment, operations, and finance requirements into robust data products.
- Contribute to engineering standards, patterns, and documentation.
Qualifications and Experience
- At least 6 years of professional data engineering experience delivering production pipelines.
- Strong experience with Snowflake’s product suite, especially core functionality.
- Hands-on experience with dbt, including testing and environment management.
- Familiarity with modern ingestion tools such as Fivetran.
- Strong grounding in Kimball and relational data modelling.
- Production-quality Python pipeline and orchestration experience.
- Experience with Git, CI/CD, and automated testing applied to data engineering.
- Experience building replayable and idempotent pipelines with backfill support.
- Financial services experience is preferred, including knowledge of audit, restatement, and as-of data concepts.
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