Power Automate Data Engineer

Vallum Associates

Leeds

10 months ago

Applications closed

Related Jobs

View all jobs

Junior Ecommerce and Marketplace Data Analyst

Data Analyst

SQL Server Data Engineer – Hybrid, Grow with Power Platform

BI and Data Engineer

Role: Junior Data Engineer

Geospatial Data Engineer (12 month FTC)

Role: Data Engineer with Power Automate

Location: London (preferred), open to Birmingham, Manchester, or Newcastle

Duration: 6+ months contract

Mandatory: Power Automate Experience & Databricks

A "Data Engineer with Power Automate" job description would typically seek a candidate with strong data engineering skills, including data extraction, transformation, and loading (ETL), combined with proficiency in using Microsoft Power Automate to automate data workflows and processes within a business system, often integrating with various data sources and applications across the Microsoft Power Platform.

Key Responsibilities:

Design, build, and maintain data pipelines using Power Automate to extract data from diverse sources (databases, APIs, flat files, etc.), transform it as needed, and load it into target systems like data warehouses, data lakes, or business applications.
Create automated workflows within Power Automate to streamline data processing tasks like data cleansing, validation, and data quality checks.
Connect Power Automate to various Microsoft services like SharePoint, Dynamics 365, Azure, and Office 365 to facilitate seamless data flow between different systems.
Implement data quality controls and data governance practices within Power Automate workflows to ensure data accuracy and consistency.
Work with business analysts, data analysts, and other stakeholders to understand data requirements, translate them into Power Automate solutions, and deliver actionable insights.

Required Skills:

Strong understanding of data warehousing concepts, data modeling, ETL processes, data quality best practices.
Extensive experience designing and developing complex workflows using Power Automate, including connectors, triggers, actions, and data manipulation.
Proficient in at least one programming language like Python, SQL, or C# for data manipulation and custom logic within Power Automate.
Familiarity with Azure data services (Azure Data Factory, Azure Data Lake, Azure SQL Database) for large-scale data processing.
Ability to analyze data using Power BI or other data visualization tools to identify trends and insights.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jan 18, 2026

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jan 13, 2026

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Jan 6, 2026

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.