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Data Engineer at Sparkland. Location Information: Remote. . We are a team of highly-driven individuals who are passionate about technology, algorithmic trading, and solving intellectually challenging problems. Being a part of Sparkland means you get to work with some of the brightest people in one of the world’s fastest-growing and most exciting industries. We are fully remote and have a flat corporate structure that values open-mindedness, entrepreneurial spirit, commitment to excellence, and continuous learning.. The Role. We are looking for a . Data Engineer. to help us build and maintain the data backbone of our trading platform. You will be working on high-volume data . pipelines. , ensuring the reliability and observability of our infrastructure, and preparing the system for upcoming ML initiatives. If you’ve worked with modern data stacks, enjoy building efficient pipelines, and thrive in environments where data precision and scalability matter, this role might be for you.. Responsibilities. . . Design and maintain robust data pipelines to support real-time and batch processing.. . Manage and optimize our Clickhouse data warehouse, including cluster performance and schema tuning.. . Ensure data quality, observability, and governance across critical pipelines.. . Collaborate with backend engineers, trading teams, and data stakeholders to align on data requirements.. . Support internal initiatives by building tooling and monitoring for business and technical metrics.. . Take ownership of scheduling and workflow orchestration (Argo, Airflow, etc.) and contribute to CI/CD automation.. . . Required Skills & Experience. . . At least 5 years of professional experience in data engineering or backend infrastructure.. . Proficiency in . Python. , including object-oriented programming and testing.. . Solid experience with . SQL. : complex joins, window functions, and performance optimization.. . Hands-on experience with . Clickhouse. (especially the MergeTree engine family) or similar columnar DBs.. . Familiarity with . workflow schedulers. (e.g., Argo Workflows, Airflow, or Kubeflow).. . Understanding of . Kafka. architecture (topics, partitions, producers, consumers).. . Comfortable with . CI/CD. pipelines (GitLab CI, ArgoCD, GitHub Actions).. . Experience with . monitoring and BI tools. such as Grafana for technical/business dashboards. . . . Bonus Points. . . Experience with . AWS services. (S3, EKS, RDS).. . Familiarity with . Kubernetes. and . Helm. for deployment and scaling.. . Exposure to . data quality/observability frameworks. .. . Experience supporting . ML infrastructure. (e.g., feature pipelines, training data workflows).. . .