Data Engineer/Associate

Seattle Children's•Seattle, WA

2d•$88,786 - $161,147

About The Position

The Data Engineer responsibilities include building a data processing pipeline that collects, connects, centralizes, and curates data from various internal and external sources using a variety of languages and tools to marry systems together for the Enterprise Data Warehouse. This includes leveraging modern cloud platforms and services to develop highly scalable, secure and reliable data engineering solutions for efficiently moving and transforming data across systems. Additional responsibilities include designing, implementing, testing, and deploying cloud-based data processing infrastructure; performing work in an Agile team setting; and breaking down, estimating, and providing just-in-time design for small increments of work. This role is pivotal to the mission and vision of Seattle Children's Enterprise Analytics team to transform healthcare for children by providing patient safety, predictive analysis to cure diseases, lowering cost of treatment, etc.

Requirements

Bachelor's Degree in computer science or related field, or equivalent combination of education and experience/technical training that demonstrates analytical and technical competency.
Minimum of two (2) years technology industry or related experience, including items such as: Build highly scalable, scaled-out architectures on large scale database platforms.
Experience working in a complex data infrastructure environment.
Two (2) years of experience in a data engineering role.
2+ years of experience building highly scalable applications, specifically with coding languages such as Python and SQL (Structured Query Language).
Experience building scalable data pipelines using Spark or Spark-SQL with Airflow scheduler/executor framework or similar scheduling tools
Experience with cloud platforms with GCP preferred.
Advanced competency in SQL with ability to perform query optimization in large scale database platforms.
Experience in SDLC process with requirements gathering, analysis, architecture, design, implementation, testing, deployment and technical support.
Experience with any industry standard tool for Source Control and Project Management.
Experience writing test cases and test scripts for data quality assurance.
Experience creating stored procedures and functions.
Experience developing dimensional data model with any industry standard tool.
Experience with Google BigQuery or equivalent
Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, or related field, or equivalent combination of education and technical training.
Demonstrated experience through internships, academic projects, research projects, capstone programs, open-source contributions, or equivalent hands-on technical experience.
Coursework or practical experience in databases, data structures, software development, analytics, or cloud technologies.
Experience writing SQL queries and performing basic data analysis.
Experience programming in Python or similar programming language.
Understanding of ETL/ELT concepts and data integration principles.
Familiarity with source control tools such as Git/BitBucket.
Basic understanding of cloud computing concepts.

Nice To Haves

Preferred Experience in Healthcare or related industry.
Experience GCP cloud services and data warehouse stores like BigQuery
Experience productizing/automating predictive models that use R, SAS, Python, SPSS, etc.
Experience in version control and CI/CD (Continuous integration and continuous delivery) tools.
Familiarity with test-driven development methodology for analytic solutions.
Familiarity with Agile framework and test-driven development methodology for analytic solutions.
API development.
Data visualization and/or dashboard development.
Internship experience in Data Engineering, BIE(Business Intelligence Engineer), Software Engineering, AI, or related field.
Experience building data pipelines through academic, internship, research, or personal projects.
Exposure to cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure.
Familiarity with BigQuery, Snowflake, Databricks, Spark, or similar technologies.
Experience with workflow orchestration tools such as Airflow.
Exposure to APIs, data integration, or automation projects.
Completion of coursework, certifications, bootcamps, or specialized training in Data Engineering, Cloud Computing, Analytics, or AI.
Experience using generative AI tools, LLMs, or data-focused AI projects.
Healthcare or healthcare analytics experience is a plus.

Responsibilities

Build a data processing pipeline that collects, connects, centralizes, and curates data from various internal and external sources using a variety of languages and tools to marry systems together for the Enterprise Data Warehouse.
Leverage modern cloud platforms and services to develop highly scalable, secure and reliable data engineering solutions for efficiently moving and transforming data across systems.
Design, implement, test, and deploy cloud-based data processing infrastructure.
Perform work in an Agile team setting.
Break down, estimate, and provide just-in-time design for small increments of work.