Must have

• SQL

• Python

• Power BI

• Databricks

• Spark

• English (C1)

Requirements description

• Leadership - Independent. Driven. Get things done
• Communication - Very strong written and verbal. Comfortable in front of a client
• Business Analysis - Capture requirements, understand use cases and translate into solutions
• Domain knowledge - Retail preferred. Must know at least one complex domain
• Data modelling, transformation - Strong data modelling knowledge and ability to design complex model
• Integration and data warehousing - Have used a platform and configured it to create complete solutions for customers
• Programming and software engineering - Python or equivalent at competent level to write functions and plugins, not expert
• BI and dashboarding - PowerBI or else required - or ability to pick up rapidly
• Databases and SQL is a must have
• Technical understanding - Good technical understanding of modern architectures and leading data platforms, if possible Databricks, Spark (as a user, not an expert)
• Cloud - Should know way around at least one cloud
• Previous experience working for a system integrator, a consultancy or professional services organization to build solutions for customers would be extremely beneficial

Offer description

We’re looking for a technically strong and highly motivated Middle/Senior Implementation Engineer (Data Engineer) who will become a “super user” of the platform and help deliver end-to-end, data-driven solutions for enterprise clients.

This is a hybrid role combining business analysis, data transformation, system integration, and configuration. You’ll work across the full solution lifecycle — from requirements gathering to platform setup, data modeling, and validation — helping clients unlock the full value of the platform through advanced configuration and light custom development.

You’ll work closely with both internal teams and client stakeholders, leveraging your communication, analytical, and problem-solving skills to deliver impactful outcomes.

Your responsibilities

1. Data analysis, exploration, testing and validation, interacting with the client to understand data structures and use cases
2. Configure connectors (e.g. Shopify, Akeneo, Bloomreach, Optimove, etc)
3. Setup client’s Platform workflows/data transformations
4. Functions and transforms - configure, write new plugins
5. Canonical data models (XDM) and mapping- configure, extend and map the data using Platform portal, Jsonata or code plugins
6. Setup Platform data hubs (data mapping, domain specific component)

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