Data Engineer, Vizion

• 5+ years of experience in a data engineering role
• 3+ years of programming knowledge with Python
• Strong experience with AWS, Snowflake or similar cloud data platforms
• Ability to analyze data with Python or R
• Advanced knowledge of database design
• Experience building and supporting data collection, data transformation, and data structures
• Proven track record of successfully working with ETL and/or similar processes
• Experience demonstrating agile working practicesInquisitive, pragmatic, and exploratory mindset
• Sense of humor, team player, good interpersonal skills
• Multiple years of experience with data modeling and database performance
• Ability to build complex data transformations and understanding to discern when that is needed
• Highly organized both in terms of work management and written code
• Highly proficient in SQL
• Experience or strong interest in data science & machine learning
• Passion for working with stakeholders and curiosity about the industry
• Eagerness to pitch in on analysis projects and data engineering tasks when needed

Desirable

• As the Data Engineer - Analytics, you will be an integral part of the Data Analytics team. We will look to you to lead technical work and projects which enable Vizion to make more and better data-driven decisions
• With the broader team, the engineer is responsible for the creation and maintenance of the end-to-end data infrastructure
• This encompasses capturing the correct data, designing a data model that fits the needs of our data consumers, and creating the infrastructure to support efficient data processing
• As an early employee, you'll be a key member in building our core infrastructure and have a strong voice in decision-making processes
• You'll be focused on Vizion’s data program which supports the core product for our customers, internal business needs, and industry-level analytics — helping to accelerate the digital transformation of supply chain & logistics companies around the world
• The mission of this role is to make reliable, well-structured data available for reporting, analysis, machine learning, and data products
• While multiple team members will participate in building data models, the Analytics Engineer will take the lead on data architecture, processes, and best practices
• This role will report to our Director of Analytics and will partner with engineering, product, sales, support, and marketing teams
• Work within a small team of passionate data scientists, backend/frontend engineers, and data analysts
• Build data pipelines that support the needs of an internal/external facing data product as well as a business-centric data warehouse
• Assist in the architecture and build of the data ecosystem including different components such as relational/non-relational databases and big data stores for an optimal data pipeline
• Build data systems and pipelines required for optimal ETL/ELT of data from a wide variety of data sources using ETL/ELT methods/tools and cloud technologies
• Assist in defining standards for naming, governing, managing, modeling, transforming, storing, and data deliveryDeliver updates on a regular basis as to the performance of data pipelines, quality of data, and gaps that exist with possible solutions to address
• Help identify better practices, tools, and relevant trends that can positively influence the data operations across the businessBuild for scalability as we rapidly expand our data footprint and systems
• Assist in building algorithms that allow for the quick and efficient retrieval of large amounts of data
• Ensure database security, integrity, stability, and availability
• Create processes to enable data quality and cleanliness throughout the pipeline
• Collaborate with stakeholders across engineering, product, and other data teams to understand how data models are used today while building for tomorrow
• Monitor and optimize for performance (materializations, etc.)
• Weigh in on tool and process decisions about our data stack
• Collaborate with other engineers to ingest new data sources, including third-party data
• Develop high-impact data layers
• Partner with data scientists, data engineers and business leaders to rapidly experiment and deploy predictive tools and insights
• Analyze and model data to assist team members and other departments with answering questions about our data
• Maintain the data catalog, a scalable resource to support self-service and single-source-of-truth analytics
• Test, review, modify, and debug new and existing systems
• Become a data subject matter expert, identifying data gaps and mitigation approaches
• Data archival solutions
• Service and consumption layers and patterns for internal/external users