Staff Data Engineer

Posted 3 days agoViewed

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💎 Seniority level: Staff, 7+ years

📍 Location: United States

💸 Salary: 160000.0 - 230000.0 USD per year

🔍 Industry: Daily Fantasy Sports

🗣️ Languages: English

⏳ Experience: 7+ years

🪄 Skills: LeadershipPythonSQLCloud ComputingETLGCPGitKafkaKubernetesAirflowData engineeringGoPostgresREST APISparkCI/CDMentoringDevOpsTerraformData visualizationData modelingScripting

Requirements:

7+ years of experience in a data Engineering, or data-oriented software engineering role creating and pushing end-to-end data engineering pipelines.
3+ years of experience acting as technical lead and providing mentorship and feedback to junior engineers.
Extensive experience building and optimizing cloud-based data streaming pipelines and infrastructure.
Extensive experience exposing real-time predictive model outputs to production-grade systems leveraging large-scale distributed data processing and model training.
Experience in most of the following: SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable, Materialize, AlloyDB, etc
Replication/ELT services: Data Stream, Hevo, etc.
Data Transformation services: Spark, Dataproc, etc
Scripting languages: SQL, Python, Go.
Cloud platform services in GCP and analogous systems: Cloud Storage, Cloud Compute Engine, Cloud Functions, Kubernetes Engine etc.
Data Processing and Messaging Systems: Kafka, Pulsar, Flink
Code version control: Git
Data pipeline and workflow tools: Argo, Airflow, Cloud Composer.
Monitoring and Observability platforms: Prometheus, Grafana, ELK stack, Datadog
Infrastructure as Code platforms: Terraform, Google Cloud Deployment Manager.
Other platform tools such as Redis, FastAPI, and Streamlit.
Excellent organizational, communication, presentation, and collaboration experience with organizational technical and non-technical teams
Graduate degree in Computer Science, Mathematics, Informatics, Information Systems or other quantitative field

Responsibilities:

Enhance the capabilities of our existing Core Data Platform and develop new integrations with both internal and external APIs within the Data organization.
Develop and maintain advanced data pipelines and transformation logic using Python and Go, ensuring efficient and reliable data processing.
Collaborate with Data Scientists and Data Science Engineers to support the needs of advanced ML development.
Collaborate with Analytics Engineers to enhance data transformation processes, streamline CI/CD pipelines, and optimize team collaboration workflows Using DBT.
Work closely with DevOps and Infrastructure teams to ensure the maturity and success of the Core Data platform.
Guide teams in implementing and maintaining comprehensive monitoring, alerting, and documentation practices, and coordinate with Engineering teams to ensure continuous feature availability.
Design and implement Infrastructure as Code (IaC) solutions to automate and streamline data infrastructure deployment, ensuring scalable, consistent configurations aligned with data engineering best practices.
Build and maintain CI/CD pipelines to automate the deployment of data solutions, ensuring robust testing, seamless integration, and adherence to best practices in version control, automation, and quality assurance.
Experienced in designing and automating data governance workflows and tool integrations across complex environments, ensuring data integrity and protection throughout the data lifecycle.
Serve as a Staff Engineer within the broader PrizePicks technology organization by staying current with emerging technologies, implementing innovative solutions, and sharing knowledge and best practices with junior team members and collaborators.
Ensure code is thoroughly tested, effectively integrated, and efficiently deployed, in alignment with industry best practices for version control, automation, and quality assurance.
Mentor and support junior engineers by providing guidance, coaching and educational opportunities
Provide on-call support as part of a shared rotation between the Data and Analytics Engineering teams to maintain system reliability and respond to critical issues.

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