Roku Senior Data Engineer, Data Governance Lead at Roku to lead design and development of enterprise data quality and governance frameworks. Develop AI/ML solutions and build scalable data pipelines in cloud environments.
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 to ensure accurate, compliant, and discoverable data assets across Roku.
Lead design and implementation of custom integrations within data discovery frameworks like Datahub for metadata-driven lineage and search.
Develop AI/ML-based solutions for data quality, anomaly detection, and automated data discovery.
Extend and customize open-source data quality frameworks and tools like Deequ and Great Expectations to ensure quality across critical datasets.
Design and implement robust batch and real-time data pipelines using Apache Spark to support large-scale daily data volumes exceeding 10TB and real-time pipelines processing millions of events per minute.
Build and maintain enterprise data warehouses and data solutions in AWS/GCP cloud environments using big data technologies, ensuring operational stability and optimal performance at petabyte scale.
Design and develop cloud-agnostic tooling, CI/CD pipelines, serverless compute, and containerized deployments with Kubernetes.
Provide technical leadership by mentoring and directing engineers and consultants, driving roadmap and sprint planning, and defining long-term strategies for data quality and governance initiatives.
Collaborate with product managers, data analysts, data scientists, ML engineers, backend engineers, and business stakeholders to translate requirements into scalable data models and data solutions, improve event logging frameworks, enforce data governance standards, and strengthen data foundations for enterprise-wide adoption.
Influence enterprise-wide technical direction, lead cross-functional efforts to unify fragmented governance practices, drive build-versus-buy decisions, define data asset prioritization frameworks for governance investment, and establish long-term strategies for data engineering, quality, and governance.
You are required to have
Qualification
Technical leadLeading cross-functional projectsData discovery platform like DatahubData Modeling
Required
Technical lead, driving architecture decisions, setting best practices, and guiding teams on data engineering and data governance initiatives in large-scale, consumer-facing digital companies.
Design and implementation of enterprise-grade data quality, discovery, and governance frameworks and tools from scratch.
Developing Artificial Intelligence (AI) and Machine Learning (ML) solutions to solve data quality, anomaly detection, and data discovery problems.
Leading cross-functional projects, partnering with engineering, product, analytics, and data science teams to deliver enterprise-wide data quality and governance solutions, ensuring successful rollout and adoption across the organization.
Collaborating with software engineering teams to improve event logging, enforce quality at the source, and manage complex logging frameworks.
Managing and mentoring global engineering teams and consultants day-to-day, including roadmap and sprint planning, and driving product roadmap priorities for data governance initiatives.
Data discovery platform like Datahub, including developing custom integrations and data quality frameworks like Deequ or Great Expectations.
Data Modeling, SQL, Airflow, Python, and Java/Scala (including Spark UDFs) and Big Data technologies Spark, HDFS, YARN, Hive, Kafka, Flink, Elastic Search, Grafana and Presto.
Developing and optimizing distributed data pipelines using Apache Spark (including Spark on Kubernetes), supporting 10TB+ daily data volume and real-time pipelines processing millions of events per minute.
System design and implementation of multi-tier, highly scalable, distributed data pipelines and data warehouses in AWS or GCP cloud environment and cloud-agnostic tooling, CI/CD pipelines, serverless compute, and containerized deployments using Kubernetes.
Up to one day per week telecommuting may be permitted.
