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📐Analytics Engineer

Developer Level 2 (DataBase)

Derivco · Durban, Kwazulu-Natal, South Africa
// classified as
Analytics Engineer (dbt, semantic layer, transformation.)
posted
12d ago
location
Durban, Kwazulu-Natal, South Africa
languages
python, sql
tools
bigquery, clickhouse, metabase
> stack
pythonsqlbigqueryclickhousemetabaseredshifttableau
> description

 We are looking for a hands-on Data Engineer to own and evolve our data warehouse — currently SQL Server, migrating to ClickHouse. You will be responsible for the full data lifecycle: from landing and transforming raw data, through to building clean, well-named access layers that enable analysts and business users to self-serve insights via tools like Metabase or LLM-based query interfaces.


Key Responsibilities

 Own and evolve our data warehouse — currently SQL Server, migrating to ClickHouse. You will be responsible for the full data lifecycle: from landing and transforming raw data, through to building clean, well-named access layers that enable analysts and business users to self-serve insights via tools like Metabase or LLM-based query interfaces.

 

 Required Skills & Experience

 

  - Strong SQL — query writing, schema design, indexing, and performance tuning
  - Hands-on experience with a columnar or analytical database (ClickHouse, BigQuery, Redshift, or similar)
  - Solid understanding of data modelling — star/snowflake schemas, dimensional modelling, naming conventions
  - Experience building and maintaining ETL/ELT pipelines
  - Familiarity with at least one BI or self-service tool (Metabase, Tableau, Power BI, etc.)
  - Comfortable working in a Python or dbt-based transformation layer.


Nice to Have

 

  - Direct ClickHouse experience (MergeTree engine, materialized views, query optimisation)
  - Experience enabling LLM-based data access (e.g. text-to-SQL tooling, semantic layers)
  - Exposure to data cataloguing or documentation tools


Level Expectation

 

  This is a strong intermediate role. We expect the candidate to work independently on well-scoped problems, make sound technical decisions around data modelling and access patterns, and push back constructively when requirements would lead to poor data design. They should not need architectural hand-holding but will have senior support for large-scale decisions.

 


Closing Date: 23 April 2026