Associate Deployment Engineer

Technical duties

• Write data transformation scripts and data pipelines using Python and SQL
• Configure and build process mining models and execution gap analyses within the Celonis Platform
• Integrate machine learning models and large language model (LLM) architectures into client solution workflows
• Design and document technical specifications and operational metrics for enterprise stakeholders
• Perform data validation and quality control checks on automated decision workflows

Project responsibilities

• Process discovery implementations mapping enterprise digital twins using the Celonis Context Model
• Data transformation pipelines integrating disparate customer IT systems and databases via Python and SQL
• AI-forward solution deployments incorporating machine learning and LLM agents to automate enterprise decision workflows

A typical day (Karyvio’s outline for this kind of role — not an employer commitment)

• Attend daily stand-up meetings with the project consulting team to align on task priorities and deliverables.
• Write and optimize SQL queries and Python scripts to transform customer data for process mining models.
• Participate in client-facing discovery workshops to map out existing business workflows and operational bottlenecks.
• Build, test, and refine AI-driven analyses and dashboards on the Celonis Platform under the guidance of senior consultants.
• Document technical solutions and review code or workflow outputs to maintain high quality control standards.

Documents to keep ready

• Resume/CV
• Cover Letter

Skills that matter, and why

• Python

  Essential for writing custom data transformation scripts, automating repetitive workflows, and handling backend data manipulation tasks within customer engagements.

  How to learn it: Focus on data structures, pandas library, and file input/output operations through building small automation scripts.

• SQL

  Required to query relational databases, inspect raw operational data, and perform complex data transformations necessary for process discovery.

  How to learn it: Practice advanced joins, window functions, and aggregations using real-world datasets on online platforms.

• Machine Learning

  Helps in understanding predictive models and AI-forward solutioning deployed on top of enterprise data platforms.

  How to learn it: Study core regression and classification algorithms using scikit-learn and understand how models integrate into larger architectures.

• AI Architectures

  Enables you to comprehend how modern enterprise AI agents and LLM integrations interact with core business systems and process data.

  How to learn it: Read technical whitepapers on enterprise AI frameworks, vector databases, and retrieval-augmented generation (RAG) pipelines.

• Data Transformation

  Crucial for cleaning, structuring, and mapping raw transactional system data into standardized formats compatible with analytical platforms.

  How to learn it: Work on ETL pipeline projects that ingest raw messy CSVs or database exports and transform them into clean analytical tables.

• Process Mining

  The core operational methodology used to uncover bottlenecks, inefficiencies, and variations in enterprise workflows using event log data.

  How to learn it: Explore foundational process mining concepts, understand event logs, and review public documentation on process discovery techniques.

• Stakeholder Management

  Critical for gathering functional requirements, presenting technical findings to business leaders, and building strong client alignment.

  How to learn it: Practice structuring technical summaries into high-level business impact talking points for non-technical audiences.

• Technical Documentation

  Ensures built solutions are maintainable, transparent, and easily handed over to customer teams or internal stakeholders.

  How to learn it: Build a habit of writing clear README files, system architecture diagrams, and concise deployment logs for your personal or academic projects.

Preparation plan

1. Strengthen your Python programming skills, focusing on data manipulation libraries such as Pandas and NumPy.
2. Master writing complex SQL queries, joins, and aggregations for efficient data transformation.
3. Learn the fundamentals of process mining, workflow automation, and enterprise digital twins.
4. Review basic machine learning concepts and how AI models integrate into business operations.
5. Work on structuring technical documentation and building clear presentations to communicate insights to non-technical stakeholders.
6. Practise end-to-end data analysis workflows from raw data ingestion to business recommendation generation.

Project ideas to show you can do the job

Interview guidance

• Brush up on core data structures, algorithms, and fundamental data wrangling techniques using Python and SQL.
• Prepare to discuss academic or internship projects where you bridged technical analysis with business value or stakeholder outcomes.
• Familiarize yourself with basic concepts of process mining, workflow optimization, and how enterprise AI architectures function.
• Practice communicating complex technical findings in simple business terms to demonstrate strong consulting and stakeholder management aptitude.