Senior Data Engineer

Firm Overview

Lunate is a new Abu Dhabi-based, Partner-led, independent global alternative investment manager with more than 250 employees and $115 billion of assets under management. Lunate invests across the entire private markets spectrum including buyouts, growth equity, early and late-stage venture capital, private credit, real assets, and public equities and public credit. Lunate aims to be one of the world’s leading private markets solutions providers through SMAs and multi-asset class funds, seeking to generate best-in-class risk-adjusted returns for its clients.

Job Purpose

The Senior Data Engineer will be the first dedicated internal data engineering hire in Lunate’s Data & AI function. This role is responsible for building dependable ingestion and transformation pipelines into Snowflake, shaping high-quality, well-modelled datasets that power reporting, operations, and AI-enabled products. You will work closely with the Head of Data & AI and partner with Infrastructure (who own Snowflake account and platform setup) to deliver a modern, data-as-code engineering practice with strong controls, testing, and repeatability, supporting both BI and AI/Agentic workloads.

Key Duties and Responsibilities

• Build and own ingestion pipelines into Snowflake using modern ELT patterns (Fivetran or equivalent, plus custom ingestion where needed), handling schema drift and source changes safely.
• Leverage AI Tools to design and automate our pipelines.
• Implement a layered data architecture (raw, standardised, business) that preserves immutability, auditability, and supports reprocessing and backfills.
• Develop transformations as version-controlled code (SQL and Python) using dbt or equivalent tooling.
• Apply pragmatic data modelling practices, using dimensional (Kimball) or relational approaches where appropriate.
• Build data quality and reconciliation into pipelines, with automated checks and visible quality signals.
• Operate pipelines like software: CI/CD, automated testing, peer review, and controlled promotion across environments.
• Optimise Snowflake usage for performance and cost, working with Infrastructure on governance controls.
• Partner with BI practitioners and AI engineers to deliver AI-ready datasets with consistent semantics and clear lineage.
• Collaborate with investment, operations, and finance teams to translate requirements into robust data products.
• Contribute to engineering standards, patterns, and documentation as the data function scales.

Qualifications and Experience

• 6+ years of professional data engineering experience delivering production pipelines.
• Strong experience with Snowflake’s full suite of products – especially core functionality.
• Hands-on experience with dbt, including testing and environment management.
• Familiarity with modern ingestion tools such as Fivetran.
• Strong grounding in data modelling fundamentals (Kimball and relational).
• Python-native, comfortable writing production-quality pipeline and orchestration code.
• Strong software engineering discipline applied to data (Git, CI/CD, automated testing).
• Experience building replayable, idempotent pipelines with backfill support.
• Financial services experience preferred, with understanding of audit, restatement, and as-of data concepts.