Vivian is hiring a Lead Data Engineer to set the technical direction for the data engineering team and own the strategy, maintenance, and operations of our data platform. You will lead a team while staying hands-on with architecture decisions and technical leadership, building the foundation for all analytics and ML initiatives.

What You'll Do

Set the technical direction for the data engineering team and own the strategy for the data platform.
Build and maintain feature stores and ML infrastructure to power machine learning models.
Own the data platform roadmap and align with engineering and product leadership.
Implement improved monitoring, alerting, and observability for data pipelines.
Begin design and architecture work for the feature store initiative and launch its first iteration.
Establish best practices for data quality, testing, and documentation across the team.
Drive tech stack decisions for real-time data infrastructure.
Build a robust, scalable data platform serving as the foundation for analytics and ML.
Power real-time feature pipelines that enable personalized experiences for millions of users.
Grow and develop a high-performing data engineering team.

What We're Looking For

7+ years of professional experience in data engineering or software engineering.
2+ years in a leadership or management role.
Proven experience designing and operating data platforms at scale.
Strong experience with Python and SQL.
Deep expertise with cloud data warehouses like Snowflake, BigQuery, or Redshift.
Experience building and maintaining feature stores and ML infrastructure.
Knowledge of AWS technologies like DynamoDB, Lambda, SageMaker, and Kinesis.
Experience with Infrastructure-as-Code tools like CloudFormation or Terraform.
Experience with Airflow, DBT, and data orchestration best practices.
Hands-on experience with streaming data pipelines (Kafka, Kinesis, or similar).
Track record of mentoring engineers and building high-performing teams.
Strong communication skills to translate technical concepts for non-technical stakeholders.

Technical Stack

Languages: Python, SQL
Cloud Data Warehouses: Snowflake, BigQuery, Redshift
AWS Services: DynamoDB, Lambda, SageMaker, Kinesis
Infrastructure Tools: CloudFormation, Terraform
Orchestration & Workflow: Airflow, DBT, Kafka

Team & Environment

You will lead a centralized group of talented data professionals, with experience across BI and analytics, engineering, data science, and machine learning.