Analytics Engineer

About This Role

An Analytics Engineer will own data models in the company's dbt codebase from end to end, from raw loaded tables to the marts that power Metabase and Power BI dashboards. This is a hands-on build role focused on data modelling, orchestration, and warehouse performance, working with a small, senior team.

Responsibilities

• Build and own dbt models across staging, intermediate, fact/dim, and reporting layers, including incremental models with merge strategies, SCD-style history tables, and change-log driven refresh logic
• Model the domain properly, including clients, agents, products, chats, leads, traffic, and revenue, with dimensional thinking, sane grain, and keys that survive contact with real data
• Write dbt tests and data-quality checks (generic, singular, and custom) so that a bad upstream load fails loudly instead of quietly reaching a client dashboard
• Build ingestion with dlt, handling new sources, incremental/persistent vs non-persistent load patterns, schema evolution, and migrating remaining legacy connectors
• Write Python where tooling stops, pulling from third-party APIs that dlt doesn't cover, handling pagination, rate limits, and retries, landing data in Redshift, and calling existing AI services for enrichment
• Orchestrate in Airflow 3, authoring and maintaining DAGs, wiring dependencies between load, transform, and report stages, and keeping run times and retries under control
• Build dashboards in Metabase and Power BI on top of your own models, and work directly with commercial and operations stakeholders on what they actually need
• Care about performance, including distribution and sort keys, join strategy, incremental windows, and diagnosing performance changes
• Work like an engineer, using branches, PRs, code review, pre-commit checks, and documented models, ensuring nothing reaches production straight from a SQL client

Requirements

• 1+ year in an analytics engineering, data engineering, or BI engineering role (internships and academic projects do not count)
• Strong SQL, including window functions, CTEs, multi-table joins, aggregation at the right grain, and the ability to read someone else's 200-line query and explain what it does
• Working Python, including requests/pandas, clean functions, error handling, and logging, sufficient to own a script that runs unattended every morning
• Data modelling fundamentals: facts vs dimensions, grain, and keys that hold up once the data gets messy
• A branch-and-pull-request workflow, where work reaches production through code review, not a SQL client
• Clear written and spoken English, as you will talk directly to non-technical stakeholders

Nice to have:

• Hands-on production dbt, including ref/source, materializations, incremental models, tests, macros, and Jinja