Data Analytics Engineer, SecurityScorecard

• You are great at identifying and solving problems
• You work independently to define, build, and analyze data and metrics
• You like solving complex challenges and delivering measurable impact
• You exhibit great judgment and sharp business and product instincts that allow you to differentiate essential versus nice-to-have and to make good choices about trade-offs
• You have a point-of-view on the modern data stack and how to implement a stage-appropriate stack at a scaling start-up
• You’re comfortable with both working in the trenches writing SQL and with taking a big-picture view while advising senior leadership on data insights
• More likely to be told “Slow Down” than “Jump in”
• Hungry, humble, scrappy, and will thrive in fast-paced environments and manage multiple priorities simultaneously
• You get a kick out of digging deep into the data, thinking from first principles, and delivering results that move the needle
• You have a process-oriented mindset and enjoy doing whatever it takes to execute complex projects
• You want to promote and evangelize a data-driven mentality at the company and are ready to teach those who want to learn
• 6+ years experience in an analytics engineering, data engineer, or business intelligence role
• Solid experience with data architecture, data warehousing, and ETL pipelines
• Experience with at least one BI platform (Tableau, Looker, Sigma, etc.)
• Proficient in writing legible, well-documented, and performant SQL (your code doesn’t just work, it’s optimized for its use case)
• Exceptional verbal & written communication. You’re able to convey complex, technical topics to an array of stakeholders in a digestible and compelling manner
• Strong project management skills with experience working with cross-functional teams and influencing stakeholders at all levels of the organization

Desirable

• SecurityScorecard is looking for our first Analytics Engineer to build out our data function.
• This function will provide SSC with accurate and up-to-date data models to inform our operational and strategic decisions
• Founding Analytics Engineering Function
• Build a centralized analytics engineering function that partners with individual engineering teams to design, create, and maintain data models that capture key business metrics
• Partner with Engineering to write SQL for ETL flows
• Improve data collection and reporting processes to automate manual workflows and ensure data quality
• Make Data Available to Practitioners
• Drive best-in-class data processes and standards that ensure data is accurate, up-to-date, reliable, and easily accessible to all parts of the organization
• Partner with the Engineering Infrastructure team to understand, evolve, and maintain our data stack (warehouse, ETL, BI tool, etc.)
• Expand Data Culture
• Collaborate with cross-functional stakeholders to understand data needs, identify opportunities for data-driven decision-making, and develop solutions to capitalize on these
• Partner with data analysts to create analysis, dashboards, and visualizations that uncover valuable insights and drive informed decisions
• Lead by example with compelling visualizations and data storytelling