3 days ago
Staines-upon-Thames, United KingdomMid Level
Responsibilities
- Build and maintain ETL/ELT pipelines that transform carrier, shipment, invoice, and ERP data into the canonical Data Connect model.
- Develop document data extraction capabilities for airway bills, purchase orders, and commercial invoices using OCR and parsing techniques.
- Extend carrier and ERP integrations and help onboard new data sources.
- Implement data contracts, validation, reconciliation, testing, version control, and observability for pipeline logic.
- Own and reliably operate an assigned portion of the pipeline estate within performance and quality SLAs.
- Contribute to dimensional models, including Kimball star schemas, snowflake schemas, and normalized models, under senior engineering direction.
- Debug pipeline and data-quality issues quickly, document solutions, and enable analysts to self-serve.
- Collaborate with the Lead Data Engineer, Data Scientists, Software Engineers, and analysts on pipeline design and delivery.
- Use LLMs and AI coding tools for rapid debugging and prototyping.
Requirements
- Approximately two years of production experience as a Data Engineer or in a closely adjacent backend/data role.
- Strong SQL skills and comfort with dimensional data modeling, 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 tooling such as Pub/Sub, Kafka, or Datastream.
- Proper use of version control and tests for pipeline logic.
- Ability to take ownership of assigned work while seeking direction on architecture decisions and communicating risks proactively.
- Preferred experience with OCR or document-parsing tools such as Tesseract or Textract, or with supply-chain documents.
- Preferred exposure to Terraform, Kubernetes, logistics or supply-chain data, B2B SaaS data, ML, NLP, generative AI, forecasting, or analytics pipelines.
Benefits
- Flexible hybrid work opportunities that support varied work arrangements and inclusive workplace experiences.
- Opportunity to work on a unified logistics data platform whose improvements benefit the broader IFS.ai Logistics platform.
- Opportunity to learn from senior engineers and grow into greater ownership over time.
- Global, diverse work environment with a stated commitment to sustainability, inclusion, collaboration, and employee creativity.
Tech Stack
Apache KafkaApache SupersetdbtDjangoFastAPIGoogle BigQueryGoogle Cloud PlatformKubernetesPythonSnowflakeSQLTerraform
Categories
Data Engineering
About IFS
IFS builds IFS Cloud, an enterprise suite covering ERP, EAM, FSM, SCM, and project/service management for manufacturers and asset‑intensive organizations. Its business model centers on software subscriptions and services for cloud and hybrid deployments, plus industry-specific modules and consulting. Founded in 1983 and headquartered in Linköping, Sweden, the company is privately held under EQT ownership and operates globally across industrial and service-focused markets.