Technical Architect

[When you apply, please include a brief description of why you would be a good fit and the sum of 42 and 56 in your application.]

The Technical Architect is the key technical leader on any Data Engineering project.  They are responsible for the overall delivery of technical foundations of a project, and ensuring all work is done to our standards. This includes expertise and oversight as needed by the team during each phase of our project work.  Beyond project work, the Technical Architect participates in the review of new candidates, and coaching/training of new hires.

Mammoth Growth data projects typically involve a data warehouse (Snowflake, Redshift, Bigquery etc.) a modeling layer (dbt), and a visualization tool (Looker, Tableau, etc.).  Deep understanding of each of these components is an important part of the role.

Mammoth Growth is the leader in building modern reporting warehouse solutions.  Our clients span virtually every industry and almost every size - from Series A venture-backed startups to publicly traded Fortune 100 firms.  This is a great opportunity to work with the best on some of the most interesting projects available.

Responsibilities

- Strategic Leadership:

• Doing what it takes to move the project toward its goals
• Being the go-to expert in the room of how we are going to implement the solution
• Anticipating future problems and proactively mitigating them

- Technical Leadership

• Delegate appropriately
• Clearly articulate milestones and deliverables
• Enforce standards
• Code reviews 

- Project Collaboration (principally with the Sr. Consultant on a project)

• Provide info / feedback on requirements documents
• Defining the architecture and technical requirements
• De-risk future problems
• Assessing LoE accurately

- Sales and Renewal Collaboration (principally with the Solutions Architect on a project)

• Flag project / relationship risks early on
• Communicate expansion opportunities & project ideas
• Join sales / renewal calls to evangelize the Mammoth Process to technical stakeholders

- Candidate Evaluation & Training

• Review candidate SQL tests
• Lead new modeller training reviews
• Coach new modellers on new projects

- Custom integration architecture

• Design, propose, scope necessary custom architecture when required
• Encourage client to use off-the-shelf service when appropriate
• Build / develop custom solution when required

Required Working Knowledge

- SQL Mastery

• Read and understand any SQL
• Identify anti-patterns
• Develop positive patterns
• Generate and interpret sql execution plans / profiles

- Data Architecture

• Learn Mammoth Growth core data modelling principles
• Designing data architecture
• Set-based model design vs procedural/linear
• Converting business requirements into a tech solution
• Learn, evangelize and enforce Mammoth Process
• Leadership and expertise in the use of dbt or other tools

- Database Technology

• Snowflake vs BigQuery vs Redshift vs Hadoop/Hive/Presto/Athena vs …
• How cloud data warehouses work
• How to evaluate a new database technology
• Cost analysis
• Optimization

- Integrations

• DIY data ingestion into a warehouse
• Service solutions - Stitch vs Fivetran vs …
• Cost analysis
• pros/cons
• Build vs Buy cost analysis

- Engineering

• Python / NodeJS / knowledge of programming languages
• Understanding of the difference between data science and engineering
• AWS / Cloud architecture

- How to leverage services to build a complete solution

- Serverless / Lambda / Step Function / SQS / Kinesis

• Designing an architecture
• Git / Source control / CICD
• Experience with DBT a plus