Role overview
Very Good Security (“VGS”) was founded by highly successful repeat entrepreneurs and is backed by world-class investors like Goldman Sachs, Andreessen Horowitz, and Visa. Our company is building a modern approach to data security, compliance and privacy. VGS completely de-scopes clients from needing to hold sensitive data themselves, while still allowing them to get the full benefit of using & operating on any kind of sensitive data (payments, PII, access credentials, etc.). A major benefit to using our platform is that companies are able to offload liability while fast-tracking compliance certifications, including PCI, SOC2, and more. We are looking for a Senior Analytical Engineer who is passionate about delivering well defined, high quality, transformed, tested, and documented data sets in our Enterprise Dimensional Platform to empower our VGS Business teams to self-serve and answer their own questions via Data Pumps from the Data Warehouse into Business Systems or via data visualizations in the Business Intelligence tool. In this role you will:
Partner with business teams to scope, design, execute, measure, and increase the impact of well-informed decisions based on data solutions to empower data-driven and data-informed business strategies of high complexity and scope
Research & Improve the data quality of usable data models within the Data Warehouse and report data anomalies in issues to source system owners and the Data Engineering team
Build end-to-end data solutions, including data pumps, data models, and data quality reports to enable self-service in Business Intelligence or Systems
Deliver well defined, high quality, transformed, tested, and documented data sets in our Enterprise Dimensional Platform to empower our VGS Business teams to self-serve and answer their own questions
Own, Build, Improve, Optimize and Maintain a strong Business Intelligence and Analytics Platform & Infrastructure (especially Database/Data Warehouse Platform) via version control, data quality test/reports, ad-hoc reports, and high-quality data models for self-service consumption, keeping Data Privacy & Security in mind while improving VGS overall data architecture and management practices & implementations
Peer review and craft code that follows our software development process and meets our internal standards for >
Document every decision/action in issues, pull requests, data documentation, READMEs, or in Confluence to cultivate a knowledge-sharing environment across multiple timezones
Be the subject matter expert for Database / Datawarehouse / Business Intelligence Platform design and implementation
Exert influence over the VGS Data Strategy, the roadmap for the Data Analytics Engineering team, the service level framework SLOs/SLAs for data sources/services, and the capacity planning to reduce system/man-hour cost
Represent VGS in public communications around broader initiatives, specific projects, and community contributions
Provide mentorship for junior or intermediate team members to grow in their technical responsibilities
We expect you to have:
- 5+ years of experience in Data Warehousing, Business Intelligence, and Data Architecture/Platform using strong SQL, Python skills, and data artifacts
- Experience with Enterprise Dimensional Modeling and data analytical methodologies (including Funnel Analysis, Behavioral Analysis, Benchmarking, Segmentation/Growth Reporting, Forecasting)
- Demonstrate capacity to clearly and concisely communicate complex business logic, technical requirements, and design recommendations through iterative solutions
- Familiarity with (Git) version control, command line, and AWS Data products
- Strong interest in data quality and detail-oriented with basic math & statistical knowledge
- Experience working with cross-functional stakeholders in a highly agile, intensely iterative environment with a positive, self-motivated, and solution-oriented mindset
- English - Advanced
Even better if you know a bit about:
Experience developing data pipelines, ELT/ETL processes, and Data Modeling/Architecture best practices
Familiarity with Matillion, Argo Workflows, and Singer
Experience writing production code for automated models, especially for Enterprise Data Models in Enterprise Data Platforms
Experience with Sisense for Cloud Data Teams (formerly known as Periscope), AWS Redshift, Superset, JupyterHub, Sagemaker, Athena, Redash, Google Suite, Anaconda-Navigator, Docker, GitHub, and Visual Studio Code
Interest in machine learning, data mining, and natural language processing