Data Modeling Analyst, Sr. jobs in Orange, CA

As the Sr. Data Engineer, you will report to the Director, Data and Machine Learning, as well as continuously collaborate with key stakeholders across the business to solve the most important technical problems.

Responsibilities

• Collaborate with cross functional partners (Product Managers, Data Scientists, Machine Learning Engineers, Software Engineers, Business teams) to build data products

• Develop, optimize, and maintain complex ETL processes for moving and transforming data.

• Champion code quality, reusability, scalability, security and help make strategic architecture decisions

• Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices

• Develop APIs and Microservices to expose and integrate data products with software systems

Required Experience & Skills

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Engineering, Operations Research, or other quantitative field

• 5 years of experience as a Data Engineer writing code to extract, process and store data within different types of data stores (Snowflake, Postgres, DynamoDB, Kafka, Graph databases)

• Strong programming skills in Python and understanding of core computer science principles

• Experience with building batch and streaming pipelines using complex SQL, PySpark, Pandas, and similar frameworks

• Knowledge of relational and dimensional data modeling concepts to build data products

• Experience with data quality checks, data monitoring solutions

• Experience with orchestrating complex workflows and data pipelines using like Airflow or similar tools

• Experience with Git, CI/CD pipelines, Docker, Kubernetes

• Experience with architecting solutions on AWS or equivalent public cloud platforms.

• Experience with developing data APIs, Microservices and event driven systems to integrate data products

• Strong interpersonal and verbal communication skills

Preferred Experience & Skills:

• Knowledge of data mesh concepts

• Experience in assessing and implementing new data tools to enhance the data engineering stack

• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science

• Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information

• Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools