Analytics Engineer

The Analytics Engineer will sit at the crossroads of Spreedly's business teams, Data Engineering, Data Analytics, and Business Intelligence and be responsible for bringing robust, efficient, and integrated data models to life. A successful candidate for this role can speak the language of business teams and technical teams, and is able to translate data insights and analysis needs into models powered by Spreedly’s data pipeline and platform.

This role will blend business acumen with technical expertise and transition between business strategy and data development, working alongside self-starters interested in solving real-world problems and in streamlining our internal capacity to simplify a complex environment with billions of data points.

Responsibilities

Collaborate with team members to collect business requirements, define successful analytics outcomes, and design data models
Work with Data Engineering and Business Intelligence to build trusted data models, visualizations, and recommendations that get the highest level of confidence from the business
Design, develop, and extend DBT in a repeatable, scalable way to progress Spreedly’s overall data maturity
Create and maintain architecture, systems, design, process and execution documentation
Work in partnership with the Data team to build and maintain the data dictionary and establish a strong self-service and single source of truth analytics framework to empower business users across Spreedly
Craft code that meets our internal standards for structure, style, maintainability, and quality best practices
Maintain and advocate for these standards through code review
Work with Data Engineering to create and facilitate the code review, approval, and deployment process and serve as a peer reviewer for specific database and data model schema changes
Provide data modeling expertise to all GitLab teams through code reviews, pairing, and training to help deliver optimal, DRY, and scalable database designs and queries in Snowflake
Exercise all phases of business analysis, including data quality, data analysis, data visualization, and presentation of results and deliverables
Partner with Data team members and key stakeholders on KPI development, data collection and building dashboards
Assist in the development of our SQL Modeling layer written in LookML & DBT ensuring it is performant with big data, organized, and well-implemented
Utilize data warehousing and code development best practices to enable access to information by creating and maintaining foundational data models
Participate in the creation and support of BI development standards and best practices
Encourage internal adoption and self-sufficiency of stakeholders on the Looker platform, including documentation, training users and dashboard organization
Collaborate with data engineering to mint new dimensions and measures for stakeholder use
Keep data sources fresh even while requirements and definitions change
Address data quality issues and build in alerting
Utilize code (Python/R/SQL) for data analysis

Requirements

5+ years in the data space as an analyst, engineer, or equivalent
5+ years experience designing, implementing, operating, and extending commercial Kimball enterprise dimensional models
5+ years working with a large-scale (1B+ Rows) data warehouse, preferably in a cloud environment
2+ years experience building reports and dashboards in a data visualization tool
1+ years creating project plans to identify tasks, milestones, and deliverables
Experience with Looker, Snowflake, and DBT
Experience with Fivetran (or similar tools)
Strong background in data relationships, modeling, and semantic layer
SQL expert with experience dealing with techniques (windows functions, common table expressions, UDFs, etc.) for data investigation, transformation, analysis, and optimizing for scalability and ease of maintenance
Familiarity in programming/scripting and knowledge of Python is a plus
Have solid project management skills including planning work, managing details, keeping multiple tasks/projects on track, working with cross-functional stakeholders, and navigating ambiguity to deliver results
Enthusiasm for conducting reproducible analysis; code reviews, version control, and solid documentation
Bachelor's degree in a quantitative or technical field

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