OverviewResponsibilitiesOwn data problems end to end, delivery scalable, high quality, and well governed data assets that support analytics, experimentation, and strategic decision making. Design, build, and maintain robust end to end data pipelines and ELT/ELT workflows. Architect and implement data models and schemas for analytics, reporting and product use cases. Implement and enforce data quality, governance and logging standards by definind metrics, instrumentation, validation rules and anomaly detection. Collaboriate cross functionally with product, engineering, abalytics and data science teams to define measurement strategies and enable product analytics and experimentation. Design data models for efficient storage and retrieval. Improve data quality and reliability through internal tolls/frameworks to detect data quality issues.
40 hrs/week, Mon-Fri, 8:30 am - 5:30 pm. Salary: $228,467/yr.
QualificationsMinimum RequirementsMasters degree or foreign equivalent degree in Computer Science, Computer Engineering, Data Science, Statistics, IT, Electronic Engineering, or a related field, and three (3) years of related work experience.
In the alternative, the employer will accept a Bachelor's degree or foriegn equivalent degree in Computer Science, Computer Engineering, Data Science, Statistics, Information Technology, Electronic Engineering or a related field, and five (5) years of post-baccalaureate, progressive, related work experience.
Must have three (3) years of experience with/inL
- Strong understanding of data modelling, data warehousing and analytical data design concepts; Building data pipelines and micro services;
- Using Spark or Airflow to process high-volume batch and streaming data
- Cloud based data engineering experience, including working with AWS services such as S3, EMR, Kinesis, RDS or SQS
- Modeling across distributed and cloud based platforms such as Spark, Flink, Hive, Kafka, Airflow or AWS data services (Redshift/Athena/EMR)
- Programming in Python and Java
- Writing SQL in data structuring and data storage practices; and
- Applying modern software engineering practices such as Agile development, test driven development, or CI/CD to build production-grade data solutions.
Employer will accept any suitable combination of education, training or experience.
100% Telecommuting permitted.
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