Lead Data Platform Engineer

Posted 2024-11-07

💎 Seniority level: Lead

📍 Location: United States

💸 Salary: 180000 - 215000 USD per year

🔍 Industry: Digital innovation, adult entertainment

🪄 Skills: LeadershipSQLETLMachine LearningSnowflakeData engineeringData science

Proven technical leadership or management experience in data engineering, analytics engineering, or data infrastructure.
Deep expertise in ETL architecture and infrastructure tools.
Excellent knowledge of SQL and data modeling tools.
Excellent knowledge of Snowflake platform.
Experience with DBT, GCP, and AWS is nice to have.
Expertise in DataOps and security.

Lead and provide ongoing mentorship and support to the data platform team; build a culture that prepares them for high-impact contributions and encourages their professional growth.
Administer and manage Snowflake and other data infrastructure.
Serve as a subject-matter expert and decision-maker for data security, governance, and performance.
Collaborate with analytics and machine learning teams to ensure they have the tools and infrastructure to deliver game-changing data products.
Manage team’s daily operations.

Posted 2024-11-07

📍 United States

🔍 FinTech

🏢 Company: Zeal Group

Experience in data pipelines, ETL, data quality, OLAP, big data frameworks.
Experience in infrastructure and DevOps, cloud infrastructure, Terraform, Kubernetes, Docker, Prometheus.
Experience in data modeling, data warehouses, workflow managers.
Experience in backend development, API, micro-services, OLTP storages, and stream processing.
Experience with continuous integration tools such as GitLab CI, TeamCity, or Jenkins.
Experience with Python and SQL.

Lead and own the development and management of the data warehouse.
Collaborate with stakeholders and company leaders on data platform requirements.
Continuously improve data architecture for scalability and compliance with data privacy laws and standards.
Ensure automated testing, monitoring, and alerting are integrated into CI/CD processes to maintain system reliability and minimize downtime.
Evaluate and implement new data tools and technologies.
Implement proactive monitoring and automated error detection, ensuring data quality standards are maintained across systems.
Work with cross-functional teams to provide data insights, reports, and infrastructure support for strategic initiatives or ad hoc analyses.

Backend DevelopmentDockerPythonSQLETLJenkinsPrometheusCollaborationCI/CDDevOpsTerraform

Posted 2024-11-07

🔧 Requirements