• Take end-to-end ownership of the data platform's reliability, encompassing pipelines, warehousing, transformations, and observability. • Design, build, and maintain robust data pipelines using Apache Airflow, orchestrating complex workflows across batch and near-real-time workloads. • Manage and optimize the primary Snowflake data warehouse, handling schema design, clustering keys, materialized views, access controls, and strict cost governance. • Build and maintain the dbt transformation layer, including model design, incremental strategies, dependency management, and documentation. • Deploy and operate data platform services on Kubernetes (EKS), independently managing workloads, debugging pod issues, and tuning resource requests. • Provision and manage data platform infrastructure, including Snowflake resources and Airflow, utilizing Terraform. • Write high-quality SQL and Python for ETL tooling, pipeline logic, and data product delivery. • Manage PostgreSQL as a source-of-truth operational database, focusing on query optimization, indexing, replication, and migrations. • Lead the transition toward change data capture (CDC) for data ingestion, utilizing tools like Debezium to stream database changes. • Implement comprehensive observability, SLA tracking, and alerting across the data platform using Datadog. • Maintain CI/CD pipelines for DAG deployments, dbt runs, schema migrations, and container image builds. • Collaborate with analytics, product, and full-stack development teams to model clean, well-documented data products. • Navigate AWS environments (including RDS and S3) to manage data-adjacent services, read logs, and adjust scaling parameters.

• Design, build, and own mission-critical platform systems and integrations that support operations, fulfillment, data science, and other engineering teams • Partner with stakeholders and engineering leaders to define technical approaches, estimate effort, and break down large initiatives into incremental deliverables that enable faster, iterative delivery • Drive technical discussions, evaluate tradeoffs, and help the team arrive at practical solutions that balance business needs with long-term maintainability • Participate in the team’s on-call rotation, responding to production incidents, troubleshooting complex system issues, and driving improvements to reliability, observability, and operational excellence • Help raise the bar on code quality and engineering discipline through code reviews, testing, automation, technical documentation, and opportunistic refactoring • Participate in the interview process of other engineering candidates and contribute to hiring decisions

• Design, implement, and maintain scalable and reliable data infrastructure in GCP. • Build and optimize data pipelines fed by blockchain indexers to support our blockchain explorer, API, and analytics. • Design data models that effectively handle the unique privacy and cryptographic constraints of Aleo’s zero-knowledge network architecture. • Own, develop, and optimize APIs that serve data directly to our mobile privacy wallets and external products. • Manage and optimize database technologies, with a focus on Postgres and BigQuery. • Ensure data quality, reliability, and observability through monitoring, logging, and automated alerting. • Collaborate with product and engineering teams to understand requirements and deliver high-performance APIs. • Identify opportunities to optimize the performance and cost of data platforms. • Utilize infrastructure-as-code (Terraform and Helm) to deploy and manage data environments. • Help define the technical vision and evolution of our data platform and architecture.

• Design, build, and maintain data platforms and pipelines supporting analytics, data science, BI, and AI. • Lead the evolution of Jellyfish’s Databricks Lakehouse platform. • Enable enterprise-wide agentic analytics within Databricks with trusted datasets. • Help mature core Databricks platform capabilities including Unity Catalog and platform observability. • Build and maintain ingestion pipelines for high-value data. • Partner on data flows sending trusted data to other internal systems. • Collaborate with stakeholders to understand analytical needs and design platform solutions. • Create standards and reusable patterns for data modeling, documentation, testing, and governance. • Develop tools and processes to monitor data platform health, pipeline reliability, cost, performance, and usage. • Provide technical leadership as a senior individual contributor.