Celonis Data Engineer

London
3 days ago
Create job alert

Contract Opportunity: Data Engineer – Celonis Process Mining

📍 Location: Central London (Office Based)

📅 Start Date: ASAP

📄 Contract Length: 6 Months Initially

💷 Day Rate: TBC — expected around £500/day (Inside IR35)

About the Role

Our client is seeking a highly skilled Data Engineer with strong Celonis process mining expertise to join a leading financial services organisation. This role plays a pivotal part in enabling enterprise-wide process intelligence by transforming complex banking data into accurate, analysis‑ready insights.

Working within a regulated banking environment, you will design and deliver high‑quality event logs, build robust data pipelines, and optimise Celonis data models to support end‑to‑end visibility and drive operational improvement.

Key Responsibilities

  1. Data Engineering & Event Log Construction

  • Design, build, and maintain scalable event‑log pipelines for Celonis process mining.

  • Translate raw process event data (case IDs, activities, timestamps, attributes) into structured Celonis Data Models.

  • Ensure reusability, consistency, and performance across multiple processes.

  1. Data Model & Pipeline Development

  • Develop and optimise ETL/ELT pipelines from ERP and transactional banking systems.

  • Manage data ingestion, transformation, and refresh pipelines for Celonis datasets.

  • Build and fine‑tune Celonis CCPM and OCPM data models aligned to business requirements.

  • Work with large-volume transactional datasets while preserving end‑to‑end traceability.

  1. Performance, Quality & Assurance

  • Optimise SQL queries, transformations, and data models for performance at scale.

  • Conduct data validation, reconciliation, and root‑cause analysis.

  • Identify and resolve data quality issues proactively.

  1. Collaboration & Documentation

  • Partner closely with process analysts, functional teams, and business stakeholders.

  • Document data models, ETL logic, event log definitions, and technical decisions.

  • Support business users by enabling reliable, analysis‑ready datasets within Celonis.

  1. Governance & Best Practice

  • Ensure compliance with enterprise data governance, security, and audit standards.

  • Apply modern engineering best practices including version control, modular design, and pipeline monitoring.

  • Contribute to continuous improvement initiatives across the data engineering landscape.

    Your Profile

    Essential Skills

  • Proven experience in Celonis data engineering and process mining execution.

  • Hands‑on expertise with event log creation, Celonis data modelling (CCPM/OCPM), and PQL logic.

  • Strong proficiency in SQL, Python, ETL/ELT, and data modelling.

  • Experience handling high‑volume transactional datasets and performance optimisation.

    Desirable Skills

  • Understanding of process mining techniques and their analytics implications.

  • Strong documentation, analytical, and problem‑solving skills.

  • Background in banking or KYC operations is a plus.

    If you’re a data engineering professional with deep Celonis expertise and thrive in highly regulated environments, we’d love to hear from you.

    Apply now to start ASAP and play a critical role in transforming process intelligence within a major financial institution

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

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

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

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.