Key Facts

Remote From:

Anywhere

Full time

Mid-level (2-5 years)

English

Hard Skills

Other Skills

•
Communication
•
Teamwork
•
Quality Control

Roles & Responsibilities

4+ years building and operating production data pipelines
Strong Python experience (async/concurrency a plus)
Extensive AWS experience across services (S3, Glue, Athena, Redshift, Lake Formation, DMS, Lambda, Step Functions, SQS/SNS, ECS, DynamoDB, and CloudFormation)
Experience with lakehouse architectures (Delta or similar), schema evolution, partitioning, and upserts/merges

Requirements:

Build the data-serving layer: curated datasets, marts, and product-ready tables
Develop incremental/micro-batch pipelines and near-real-time ingestion (AWS DMS)
Design BI-friendly data models (star schema) and manage schemas
Build ETL/ELT in Python (Polars) and serve/query via Athena and/or Redshift

Baxter Planning

About Baxter Planning

Baxter Planning is a global leader in Service Supply Chain software, delivering a Service Experience Advantage to the world’s most innovative enterprises for over 30 years. The end-to-end BaxterPredict platform empowers organizations to optimize service parts planning, execution, and resolution, driving superior customer experiences, fostering long-term loyalty, and fueling business growth. By combining purpose-built technology, award-winning AI, decades of practitioner expertise, and a commitment to true partnership, Baxter Planning consistently delivers industry-leading outcomes for its clients. The company is headquartered in Austin, Texas, United States, with offices around the globe.

Founded: 2018

Company size: 201 - 500

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Job description

As a Data Engineer at Baxter Planning you will play a key role in building and evolving our data lake platform, supporting analytics and data-driven products across the business. You will design and operate reliable, production-grade data pipelines and curated datasets using modern AWS services and Python. This role focuses on data modeling, data quality, and near-real-time ingestion to ensure trustworthy, BI-ready data. You will work closely with engineers and stakeholders while contributing to architecture, automation, and best practices.

What you’ll do

• Build the data-serving layer: curated datasets, marts, and product-ready tables
• Develop incremental / micro-batch pipelines and support CDC near-real-time ingestion (AWS DMS)
• Design BI-friendly data models (star schema) and manage schemas
• Build ETL/ELT in Python (Polars) and serve/query via Athena and/or Redshift
• Implement data quality + observability (freshness, completeness, duplicates, schema drift, anomalies)
• Orchestrate with Airflow and AWS-native tools (e.g., Step Functions)
• Contribute to CI/CD, IaC, architecture discussions, and best practices

What we’re looking for

• 4+ years building and operating production data pipelines
• Strong Python (async/concurrency is a plus)
• Strong AWS across services like: S3, Glue, Athena, Redshift, Lake Formation, CloudWatch, DMS, Lambda, Step Functions, SQS/SNS, ECS, DynamoDB (+ CloudFormation)
• Experience with lakehouse tables (Delta or similar), schema evolution, partitioning, compaction, upserts/merge
• Solid data modeling skills (star schema) and commitment to testing & data quality
• Experience running AWS DMS in production (monitoring/troubleshooting)

What we offer

• A competitive salary
• Work in a friendly and diverse team
• private health insurance
• gym membership
• learning opportunities
• flexible benefits
• team events