Data Engineer

Zumiez Inc•Lynnwood, WA

2d•$93,000 - $124,000

About The Position

A data engineer designs, builds, and maintains the infrastructure and pipelines that enable the collection, storage, and processing of large datasets. They ensure data is reliable, accessible, and optimized for analytics and business intelligence. Their role often involves working with databases, ETL processes, and cloud platforms to support data-driven decision-making.

Requirements

Bachelor of Science in Computer Science, Computer Engineering or equivalent
1-5 years of professional experience in a data engineering role
Proficiency in SQL and Data Modeling - Strong command of SQL for querying and manipulating data, along with experience designing normalized and denormalized data models.
Experience with ETL/ELT Tools - Hands-on experience building and maintaining data pipelines using tools like Apache Airflow, Azure Data Factory, or AWS Glue.
Cloud Platform Expertise - Familiarity with cloud services (e.g., AWS, Azure, GCP) for data storage, processing, and orchestration in a scalable environment.
Programming Skills - Proficiency in languages like Python, Scala, or Java for data transformation, automation, and integration tasks.
Data Warehousing Knowledge - Experience with modern data warehouse solutions such as Snowflake, BigQuery, Redshift, or Azure Synapse.
Proficient in Software Development Lifecycle (SDLC) - Familiar with source control tools, automated testing, and continuous integration / deployment

Responsibilities

Data Pipeline Development: Design, build, and maintain scalable and reliable data pipelines to collect, process, and store data from various sources.
API Creation and Consumption: Capable of collecting data from SAAS providers published APIs and creating internal APIs for the publishing of data.
Data Modeling: Familiar with OLTP and OLAP modeling and when to use each. Capable of working with flat files, tables, and json to transform data into easy-to-use structures.
Data Quality and Validation: Implement data validation, cleansing, and monitoring processes to ensure high data quality and integrity.
Collaboration with Data Consumers: Work closely with data analysts, data scientists, and business teams to understand data needs and deliver appropriate solutions.
Tooling and Automation: Develop tools and scripts to automate repetitive tasks, improve data workflows, and support continuous integration and deployment of data solutions. Familiar with source control tools and typical software development lifecycle.