IFS

Cloud-based ERP, EAM, FSM, ESM solutions

Data Engineer

Data EngineeringPosted on Nov 10

Full-timeNunspeet, NetherlandsNo salary listed

About the Job

Requirements

Approximately 2 years of production experience as a Data Engineer or in a closely adjacent backend or data role.
Strong SQL skills and comfort with dimensional data modeling, including Kimball star schemas and normal forms.
Strong Python skills, including unit testing, functional testing, and data quality checks.
Experience with a cloud data platform such as BigQuery or Snowflake and an ELT tool such as dbt.
Experience with BI tools such as Superset, Looker, or Power BI.
Experience building APIs and comfort reading Django or FastAPI source code.
Understanding of schema evolution, partitioning, and incremental loads.
Familiarity with streaming and event tools such as Pub/Sub, Kafka, or Datastream.
Proper use of version control such as GitHub or GitLab, and writing tests for pipeline logic.
Comfort using large language models and AI coding tools for rapid debugging and prototyping.
Comfort seeking direction on architecture decisions while taking ownership of assigned work.
Curiosity about the logistics domain.

Responsibilities

Build and maintain ETL/ELT pipelines that transform carrier, shipment, invoice, and ERP data into the canonical Data Connect model.
Work on document data extraction, including optical character recognition and parsing for airway bills, purchase orders, and commercial invoices.
Extend carrier and ERP integrations and help onboard new data sources.
Implement data contract checks, validation, and reconciliation for downstream data consumers.
Keep owned pipelines within performance and quality service-level agreements.
Troubleshoot issues in owned pipelines, resolving them promptly.
Contribute to dimensional models, including star and snowflake schemas, following designs set by senior or lead engineers.
Follow team standards for testing, version control, and observability.
Work with the Lead Data Engineer, Data Scientists, and Software Engineers on pipeline design.
Document work so analysts across Product, Data Science, and Customer Success can self-serve.
Own and reliably operate a defined part of the pipeline estate within its service-level agreements.
Deliver pipeline and data features on the roadmap without close supervision.
Resolve and document debugging and data-quality issues in the assigned area.
Take on larger, less-defined problems over time.

Desired Qualifications

Experience with optical character recognition or document-parsing tools such as Tesseract or Textract, or with supply chain documents such as purchase orders, invoices, and airway bills.
Exposure to infrastructure as code such as Terraform or to Kubernetes.
Prior logistics, supply chain, or B2B SaaS data experience.
Exposure to machine learning, natural language processing, generative AI, forecasting, or analytics pipelines.

Job Description

Data Connect is the trusted, unified logistics data layer that everything else at IFS.ai Logistics depends on — carrier, shipment, invoice and ERP data from dozens of fragmented sources, turned into one model that Transport Planning & Optimization and Shipper TMS both build on. Work here has outsized leverage: fix a pipeline in Data Connect and the improvement is felt across the whole platform.

We're hiring a Data Engineer to help build and run that platform. This is a hands-on, individual-contributor role — you'll own delivery of pipelines and platform features end-to-end within your area, working under the direction of the Lead Data Engineer rather than setting platform architecture yourself. It's a strong fit if you've got a couple of years of production data engineering experience and want to grow into more ownership over time, with senior engineers around you to learn from rather than being the most senior person in the room from day one.

What you'll work on

Pipelines and ingestion — build and maintain ETL/ELT pipelines that turn carrier, shipment, invoice and ERP data into the canonical Data Connect model; work on document data extraction (OCR/parsing for airway bills, POs, commercial invoices); extend carrier and ERP integrations and help onboard new data sources; implement data contract checks, validation and reconciliation so downstream teams can trust what they consume
Platform and data quality — keep the pipelines you own within their performance and quality SLAs; when something breaks in your area, you can typically run it down in a couple of hours, not days; contribute to dimensional models (star/snowflake schemas) to a design set by a senior/lead; follow the team's standards for testing, version control and observability
Collaboration — work closely with the Lead Data Engineer, Data Scientists and Software Engineers on pipeline design; document what you build so analysts across Product, Data Science and Customer Success can self-serve rather than asking you

What success looks like

Owns and reliably operates a defined slice of the pipeline estate, within its SLAs
Ships pipeline and data features on the roadmap without needing close supervision
Debugging and data-quality issues in your area get resolved fast, and documented so others can self-serve next time
Trusted by the Lead Data Engineer to take on larger, less-defined problems over time

Qualifications

Core experience

~2 years' production experience as a Data Engineer or in a closely adjacent backend/data role
Strong in SQL, comfortable with dimensional data modelling (Kimball star schema, normal forms)
Strong in Python, including unit testing, functional testing and data quality checks
Experience with a cloud data platform (BigQuery, Snowflake or similar) and an ELT tool such as dbt
Experience with BI tools (Superset, Looker, Power BI)
Experience building APIs and comfortable reading Django or FastAPI source code

Data engineering fundamentals

Understands schema evolution, partitioning and incremental loads
Familiar with streaming/event tooling (Pub/Sub, Kafka, Datastream or similar) — you don't need to have designed one from scratch
Uses version control properly (GitHub/GitLab) and writes tests for pipeline logic
Comfortable using LLMs and AI coding tools for rapid debugging and prototyping

Ways of working

Comfortable asking for direction on architecture calls, but takes full ownership of building and running what's assigned
Clear, proactive communicator — flags risk early rather than after an SLA is missed
Curious about the logistics domain rather than treating data as generic rows and columns

Nice to have

Experience with OCR/document-parsing tools (Tesseract, Textract) or supply chain documents (POs, invoices, airway bills)
Exposure to infrastructure-as-code (Terraform) or Kubernetes
Prior logistics, supply chain or B2B SaaS data experience
Exposure to ML, NLP, generative AI, forecasting, or analytics pipelines

Tech stack

Warehouse/lakehouse: BigQuery.
Transformation: dbt.
Visualisation: Cube Cloud, Superset.
Streaming/integration: Pub/Sub, Datastream.
Languages: Python, SQL.
Infra: GCP, Kubernetes, Terraform, CI/CD, K8s.

Additional Information

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.

IFS