Job Summary:

As a Data Engineer at Ruggable, you will own, contribute, and extend the business’s data pipelines by extracting, storing, processing, and transforming large data sets. You will be a key part of a team whose goal is to build and maintain foundational data infrastructure essential to driving Ruggable’s revenue growth and accelerating user acquisition. You will work closely with technology leadership, data engineers, data analysts, data scientists, and engineering teams to build best-in-class data pipelines and processes that stitch together complex sets of data stores and drive actionable insights.

What You’ll Do:

Build processes supporting data transformation, data structures, metadata, dependency, and workload management. You will be responsible for developing and optimizing big data pipelines, architectures, and data sets. In-depth knowledge of Modeling and Design of DB schemas for read-and-write performance. Promoting Data Engineering best practices within the team and mentorship opportunities. Develop highly scalable data pipelines to load data from various source systems, and use Apache Airflow to orchestrate, schedule and monitor the workflows.

What You’ll Need to Have:

Required:

• Minimum of 3 years relevant experience as a Data Engineer
• Proficiency in scripting languages like Python or Bash
• Familiarity with version control systems such as Git
• Working knowledge of API integration or Stream-based data extraction and structured, semi-structured and unstructured file formats
• Design, develop and own robust, scalable data processing and data integration pipelines using Python, Meltano, Glue, and Spark to ingest data from a variety of external data sources to a data lake
• Experience with workflow and orchestration management tools like Airflow.
• Deep understanding of SQL with relational data stores and column-oriented database systems such as Redshift, Postgres, and CockroachDB
• Ability to interact cross-functionally with non-technical departments
• Ability to work individually or as part of a team
• Support and expand standards, guidelines, tooling, and best practices for data engineering

Preferred:

• Experience with AWS or relevant cloud tools
• Familiarity with data warehousing concepts and technologies
• Automated build and continuous integration tools Terraform, Jenkins and Github Actions 
• Knowledge of BI Tools like Tableau, Looker, etc 
• Infrastructure Logging, Alerting, and Monitoring experience with platforms like DataDog, Cloudwatch

Compensation:

• $130,000 - $150,000 per year base salary
• An annual bonus percentage that varies based on level of role
• Employer matching (up to 3% of base salary) for company sponsored 401K plan