2 months ago
Responsibilities
- Lead technical architecture, design, and development of enterprise data quality, discovery, and governance frameworks.
- Establish and enforce enterprise-wide data governance standards, policies, tooling, and processes.
- Design and implement DataHub integrations for metadata-driven lineage and search.
- Develop AI/ML solutions for data quality, anomaly detection, and automated data discovery.
- Extend and customize Deequ and Great Expectations for quality assurance across critical datasets.
- Design and operate batch and real-time Apache Spark pipelines handling more than 10TB daily and millions of events per minute.
- Build and maintain enterprise data warehouses and data solutions across AWS and GCP at petabyte scale.
- Develop cloud-agnostic tooling, CI/CD pipelines, serverless compute, and Kubernetes-based deployments.
- Mentor and direct engineers and consultants while driving roadmap, sprint planning, and long-term governance strategy.
- Lead cross-functional efforts involving engineering, product, analytics, data science, ML engineering, backend engineering, and business stakeholders.
Requirements
- Master’s degree or foreign equivalent in Data Science, Computer Science, or a related field, or a bachelor’s degree or foreign equivalent with 8 years of progressive experience.
- Six years of experience in the position or a related occupation, with the bachelor’s alternative requiring eight years.
- At least one year of experience leading data engineering and governance architecture, enterprise data quality and discovery frameworks, AI/ML data-quality solutions, cross-functional governance projects, logging frameworks, and global engineering teams or consultants.
- At least one year of experience with DataHub and data quality frameworks such as Deequ or Great Expectations.
- At least five years of experience with data modeling, SQL, Airflow, Python, Java or Scala, Spark, HDFS, YARN, Hive, Kafka, Flink, Elasticsearch, Grafana, and Presto.
- At least five years of experience developing distributed Spark pipelines, including Spark on Kubernetes, at high-volume batch and real-time scale.
- At least five years of experience designing scalable data pipelines and warehouses in AWS or GCP, together with cloud-agnostic tooling, CI/CD pipelines, serverless compute, and Kubernetes deployments.
Benefits
- Benefits may include healthcare, life, accident, disability, commuter, and retirement options such as 401(k) or pension.
- Global mental health and financial wellness support and resources are available.
- Employees receive time off in accordance with local leave policies and personal needs.
- Roku generally works in the office Monday through Thursday, with Fridays generally flexible for remote work; up to one day per week of telecommuting may be permitted.
Tech Stack
Apache AirflowApache FlinkApache HiveApache KafkaApache SparkAWSElasticsearchGoogle Cloud PlatformGrafanaJavaKubernetesPrestoPythonScalaSQLYarn
Categories
Data Engineering
About Roku
Roku builds streaming players, Roku-branded TVs and audio gear, and the Roku OS licensed to TV manufacturers. Its platform supports ad-supported and subscription streaming, including The Roku Channel and Roku Originals, and underpins a significant advertising business. Founded in 2002 and headquartered in San Jose, it is a public company trading on NASDAQ under the ticker ROKU.