Act as a glue between stakeholders, engineering teams, and researchers to drive progress on complex initiatives
Design, build, and optimize data pipelines to support high-scale data ingestion
Develop and maintain a feature store to support predictive models
Build automation tooling for data orchestration, testing, and monitoring
Architect scalable data solutions leveraging AWS services
Partner with Data Scientists, ML Engineers, and business stakeholders to create AI-powered insights

Requirements

5+ years of experience in data engineering
Hands-on experience in ETL, data architecture, and cloud-based data platforms
Strong proficiency in AWS (S3, RDS, Redshift, Glue, IAM, DynamoDB)
Expertise in distributed data processing frameworks (Spark, Databricks, Presto)
Strong Python and PySpark development skills
Experience with CI/CD pipelines (Jenkins) and data workflow automation (Airflow, Databricks Jobs)

Benefits

Health and wellness benefits starting day 1
Professional mastery allowance for learning support
Flexible co-working locations available

Applicant Tracking System Keywords

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

Hard skills

data engineeringETLdata architecturecloud-based data platformsAWSPythonPySparkdistributed data processing frameworksCI/CD pipelinesdata workflow automation

Soft skills

stakeholder managementcollaborationcommunicationproblem-solvingorganizational skills