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
hybrid model of work
flexible benefits
team events

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