Data Engineer - IFS Logistics

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.

About IFS

GCP, Kubernetes, Terraform, CI/CD, K8s.

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 is a billion-dollar revenue company with 7000+ employees on all continents.

Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™.

Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge.

At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact.

We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and understand our responsibility to reflect the diverse world we work in.

We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities.

As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We’re looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs.

With the power of our AI-driven solutions, we empower our team to change the status quo and make a real difference.

If you want to change the status quo, we’ll help you make your moment.

Join Team Purple.

Join IFS.

Responsibilities

• 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
• 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
• Ways of working
• dbt.

Requirements

• 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
• 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)
• BigQuery.
• Cube Cloud, Superset.
• Pub/Sub, Datastream.
• Python, SQL.

Preferred

• 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