<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0">
  <channel>
    <title>Data Engineer - SF Partners RSS Feed</title>
    <link>https://jobs.co.uk/job/data-engineer-sf-partners--d2956897-e230-4674-9ca3-944cab147b8b</link>
    <description>RSS feed for Data Engineer at SF Partners.</description>
    <language>en-gb</language>
    <lastBuildDate>Fri, 11 Sep 2026 22:20:32 GMT</lastBuildDate>
    <item>
      <title>Data Engineer - SF Partners</title>
      <link>https://jobs.co.uk/job/data-engineer-sf-partners--d2956897-e230-4674-9ca3-944cab147b8b</link>
      <guid>https://jobs.co.uk/job/data-engineer-sf-partners--d2956897-e230-4674-9ca3-944cab147b8b</guid>
      <pubDate>Thu, 10 Sep 2026 23:00:00 GMT</pubDate>
      <description>Location: St Helens | Salary: &amp;pound;55000 - &amp;pound;65000/annum | Type: Permanent | Data Engineer - Python / PySpark / Microsoft Fabric    £55,000 - £65,000 + Benefits  St Helens - 1-2 days per week    This is an opportunity to join an organisation at a particularly interesting point in the development of its data capability.    The business has invested heavily in modernising a sizeable legacy data estate, moving away from traditional SQL-based platforms and establishing a modern Microsoft Fabric/Azure data environment.    The foundations are now in place.    The next stage is about building on them.    As the Data Engineer, you''ll join a growing team responsible for developing the pipelines, transformations and data products that sit on top of the new platform.    Importantly, this isn''t a traditional Azure Data Engineer role dominated by ADF, stored procedures and drag-and-drop development.    The team is deliberately moving towards a more code-led engineering approach, with a strong emphasis on Python, PySpark and notebook-based development.    What you''ll be working with    The environment includes:    Python / PySpark  Microsoft Fabric  Spark and notebook development  Lakehouse architecture  Medallion / Bronze, Silver &amp; Gold patterns  Delta tables  SQL  A...</description>
      <category>Permanent</category>
    </item>
  </channel>
</rss>