Data Engineer

5+ years of professional software development experience using Python
2+ years of hands-on experience with AWS and Databricks in production environments
We are looking for mid-career professionals with a proven track record of deploying cloud-native solutions in fast-paced software delivery environments.
In addition to technical expertise, successful candidates will demonstrate:
Strong communication skills, with the ability to clearly articulate technical concepts to both technical and non-technical stakeholders (this is extremely important - please vet out accorrdingly)
The ability to work effectively with limited supervision in a distributed team environment
A proactive mindset, adaptability, and a commitment to team success
Key Responsibilities:
Design and implement AWS/Databricks solutions to process large geospatial datasets for real-time API services
Develop and maintain REST APIs and backend processes using AWS Lambda
Build infrastructure as code using Terraform
Set up and maintain CI/CD pipelines using GitHub Actions
Optimize system performance and workflows to improve scalability and reduce cloud costs
Enhance monitoring and alerting across systems using Datadog
Support field testing and customer operations by debugging and resolving data issues
Collaborate with product managers and end users to understand requirements, build backlog, and prioritize work
Work closely with data scientists to productionize prototypes and proof-of-concept models
Required Skills & Experience:
Excellent coding skills in Python with experience deploying production-grade software
Strong foundation in test-driven development
Solid understanding of cloud computing, especially AWS services such as IAM, Lambda, S3, RDS
Professional experience building Databricks workflows and optimizing PySpark queries
Preferred Experience:
Experience working with geospatial data and related libraries/tools
Experience building and operating API using AWS lambda
Familiarity with data lake architectures and Delta Lake
Experience with event-driven architectures and streaming data pipelines (e.g., Kafka, Kinesis)
Exposure to ML Ops or deploying machine learning models in production
Prior experience in cross-functional teams involving product, data science, and backend engineering teams

Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.