Data Engineer - Sales Data - Azure Databricks

Square One Resources; Greater London; Hybrid; GBP 96,000 - 111,000; Full time

Job summary

Square One Resources is seeking a Data Engineer to join a Sales Data project in London on a hybrid basis. The contract runs for four months, with a daily rate of 560 inside IR35.

You will work in an Azure Databricks environment, handling sales and product data to analyze year-on-year subscriptions growth, align data to uplift definitions, and ensure accurate data capture from Salesforce.

Qualifications

• Proficiency in Python and SQL for data manipulation and pipelines.
• Experience with Azure Databricks and Salesforce data structures is essential.
• Experience with Delta and Iceberg data formats and SQL/NoSQL databases.

Responsibilities

• Support a critical project using sales and product data to assess YoY subscriptions growth.
• Map product sets to correctly categorize retired products and replacements.
• Ensure base data set captures sales for previous and current year.
• Work in Azure/Databricks environment and understand Salesforce data architecture.

Skills

Python

SQL

Delta format

Iceberg format

Apache Spark

Apache Beam

Databricks

PostgreSQL

MongoDB

CosmosDB

SQL Server

Snowflake

Redshift

BigQuery

ETL

Azure Data Factory

Fivetran

Amazon Glue

AWS

Azure

GCP

Git

GDPR

ABAC

ML concepts

Tools

Azure Databricks

Salesforce data architecture

Databricks SQL

Snowflake

Amazon Redshift

Google BigQuery

Delta

Iceberg

SQL

NoSQL databases

PostgreSQL

MongoDB

CosmosDB

SQL Server

Apache Airflow

Fivetran

Azure Data Factory

Amazon Glue

Apache Spark

Job description

Job Title: Data Engineer - Sales Data - Azure Databricks
Location: London/hybrid
Salary/Rate: 560 per day inside IR35
Start Date: 07/09/2026
Job Type: Contract - 4 months

Company Introduction
We have an exciting opportunity now available with one of our sector-leading data analytics clients! They are currently looking for a skilled Data Engineer to join their team for a four-month contract.

Job Responsibilities/Objectives

We are seeking an experienced data analyst to support a critical business project which uses sales and product data to assess year on year data subscriptions growth by agreed business definitions of uplift, upsell, cross sell and cancellation. The work required involves mapping product sets to ensure retired products with superseded replacements are categorised correctly along with ensuring the base data set correctly captures sales for the previous year and the current year. We work in an Azure/Databricks environment so experience in this technical set up is essential along with a good ability to understand the sales data architecture (from Salesforce).

Required Skills/Experience

The ideal candidate will have the following:

• Proficiency in programming languages such as Python and SQL is essential. Python , in particular, has emerged as the language for Data and AI applications commonly used for data manipulation, building data pipelines, and developing algorithms for data processing.
• A strong understanding of data formats such as Delta and Iceberg is vital, in addition to both SQL and NoSQL databases. Data engineers should be skilled in designing, querying, and optimising data to ensure efficient data storage and retrieval. Familiarity with database technologies like PostgreSQL, MongoDB, CosmosDB, and SQL Server is also beneficial.
• Knowledge of data warehousing technologies such as Databricks SQL, Snowflake Warehouses, Amazon Redshift, and Google BigQuery is important for structuring and managing large volumes of data.
• Data engineers should be adept at Extract, Transform, Load (ETL) processes. This includes using frameworks such as the Medallion Architecture, as well as tools like Apache Airflow, Fivetran, Azure Data Factory, and Amazon Glue to automate data workflows and ensure data quality.
• Familiarity with cloud computing services, such as AWS, Azure, or Google Cloud Platform, is increasingly important. Data engineers often leverage these platforms for scalable data storage and processing capabilities.
• Knowledge of big data frameworks like Apache Spark, Beam is crucial for handling large datasets and real-time data processing.
• Understanding data modelling techniques helps data engineers design efficient data structures that meet the analytical needs of the organisation.
• Proficiency in using version control systems like Git is important for collaboration and maintaining code integrity.
• Awareness of data security practices and compliance regulations (such as GDPR) is essential to protect sensitive information and ensure ethical data handling. In addition, knowledge of data access methods, and attribute-based access control (ABAC) is vital to securing enterprise data.

While not always required, having a foundational understanding of machine learning concepts can help data engineers collaborate effectively with data scientists and contribute to the development of predictive models.

Disclaimer
Notwithstanding any guidelines given to level of experience sought, we will consider candidates from outside this range if they can demonstrate the necessary competencies.
Square One is acting as both an employment agency and an employment business, and is an equal opportunities recruitment business. Square One embraces diversity and will treat everyone equally. Please see our website for our full diversity statement.