Share ownership and evolve the end-to-end data architecture across streaming, batch, and analytical workloads
Design data models that preserve signal from unstructured and semi-structured sources
Enable machine learning workflows by defining clear contracts between raw, derived, and model-ready data
Establish standards for schema evolution, dataset versioning, reproducibility, and data quality
Partner closely with engineering teams to improve scalability, reliability, and cost efficiency
Embed applied data science considerations into architectural decisions, including feature reuse, temporal consistency, and leakage prevention
Document and communicate architectural decisions clearly across teams**

Requirements

You have eight or more years of experience working with production data systems
You have designed data architectures, not just individual pipelines
You have strong experience with streaming platforms such as Kafka or AWS Kinesis (Preferred)
You have experience with data orchestration tools such as Temporal (Preferred), Dagster or Airflow
You have experience with cloud-native data stacks, AWS preferred
You understand data modeling for analytics and machine learning use cases
You have working knowledge of machine learning workflows and feature engineering concepts
You have experience training AI Models and testing models for domain optimization
You are fluent in SQL and Python
You communicate clearly and collaborate effectively across teams in a remote environment

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data architecturedata modelingmachine learning workflowsfeature engineeringSQLPythondata qualityschema evolutiondataset versioningdata orchestration

Soft Skills

communicationcollaborationdocumentationownershipproblem-solving