• Design and implement cloud-native data pipelines and storage architectures supporting large-scale analytics workloads. • Develop and maintain infrastructure-as-code solutions using Terraform, Python, and CI/CD tools for automated provisioning and deployment. • Architect scalable, resilient cloud data environments leveraging AWS, Azure, or GCP. • Optimize data ingestion, transformation, and distribution frameworks for performance, security, and cost efficiency. • Collaborate with DevSecOps, data science, and software teams to align platform engineering with business and mission objectives. • Integrate and manage containerized workloads using Kubernetes, Docker, and related orchestration tools. • Support monitoring, logging, and observability pipelines for system reliability and uptime. • Ensure compliance with FedRAMP, NIST, and FISMA frameworks for secure federal cloud environments.

• Contribute expertise to the overall data eco-system’s engineering best practices, standards, and architectural approaches. • Collaborate in authoring, reviewing, and approving data requirements and designs, including ETL, data movement, pipelines, business intelligence, and analytics. • Work alongside a team of engineers to build and maintain a custom data platform and Master Data platform. • Participate in creating architecture and solution blueprints to meet client requirements. • Collaborate with the project management team to develop the overall implementation solution plan and actively contribute to project life cycle phases within the solution area. • Engage with data integration technologies to design and implement new solutions and processes to support evolving data management strategies. • Work closely with business and technology client representatives to gather functional and technical requirements. • Stay informed about the latest technology trends, particularly in the areas of data platforms.

• Own Mixbook’s data platform end-to-end, including ingestion, orchestration, transformation, and the Redshift data warehouse • Establish clear accountability, SLAs, and standards for critical datasets and business metrics • Stabilize and harden fragile pipelines, reducing data incidents and reactive firefighting • Define and enforce best practices for dbt modeling, data quality, and metric consistency. Deliver analytics-ready models that power Looker and downstream use cases. • Partner closely with Platform Engineering, Product Engineering, Data Analytics, and business stakeholders across Marketing, Product, Operations and Finance to align on priorities and tradeoffs • Act as the primary point of contact for data reliability and warehouse health at the executive level • Make pragmatic build vs. buy decisions and resist unnecessary replatforming or tool churn • Lead by building. Write and review production data engineering code. Set technical standards and architectural direction. • Enable faster, more confident analytics and experimentation by increasing trust in data • Set the stage for future data science, ML and AI capabilities once foundations are solid

• Write and deploy crawling scripts to collect source data from the web • Write and run data transformers in Scala Spark to standardize bulk data sets • Write and run modules in Python to parse entity references and relationships from source data • Diagnose and fix bugs reported by internal and external users • Analyze and report on internal datasets to answer questions and inform feature work • Work collaboratively on and across a team of engineers using basic agile principles • Give and receive feedback through code reviews