Data Engineer

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission.

By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale.

With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end-to-end commerce solutions combine best-in-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order.

Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.

What You'll Do: 

Data First:

• Design, develop, and maintain scalable and reliable data pipelines using modern data engineering tools and technologies.

• Help drive the re-architecture of our data warehouse to improve performance, scalability, and data quality.

• Implement data cleansing, transformation, and validation processes to ensure data accuracy and consistency.

• Collaborate with other engineers and stakeholders to define data requirements and develop data models.

Data Infrastructure Management:

• Build and maintain data infrastructure on GCP, including data lakes, data warehouses, and data pipelines.

• Optimize data storage and retrieval for performance and cost efficiency.

• Monitor data pipeline performance and troubleshoot issues.

• Implement data security and governance best practices.

Machine Learning Support:

• Prepare and transform data for machine learning models, ensuring data quality and consistency.

• Enable data access for machine learning algorithms and tools.

• Assist with basic data analysis and reporting tasks to support the AI team.

• Work with the engineering team to support ML models.

Collaboration and Communication:

• Work closely with engineers, data scientists, and product managers to understand data needs and deliver solutions.

• Document data pipelines and data models for knowledge sharing and maintainability.

• Communicate effectively with team members and stakeholders.

Team Guidance and Collaboration:

• Provide technical guidance and mentorship to the data team.

• Foster a culture of innovation and collaboration within the data team.

• Collaborate with cross-functional teams to integrate ML solutions into the Stord platform.

• Drive data democratization and promote data-driven decision-making.

What You'll Need:

Experience:

• 5+ years of experience in data engineering or a related field.

• Proven experience building and maintaining data pipelines and data warehouses.

• Experience with cloud platforms, preferably GCP.

• Experience with SQL and data modeling.

• Experience with data transformation tools such as dbt, or similar.

Technical Skills:

• Strong proficiency in SQL and Python.

• Experience with data pipeline tools (e.g., Apache Airflow, Prefect, or similar).

• Experience with data warehousing technologies (e.g., BigQuery, Snowflake).

• Familiarity with data lake concepts and technologies.

• Understanding of data engineering best practices.

• Understanding of basic machine learning concepts, data preparation techniques, and model evaluation.

• Experience with version control systems (e.g., Git).

Soft Skills:

• Strong problem-solving and analytical skills.

• Excellent communication and collaboration skills.

• Ability to work independently and as part of a team.  

• Strong attention to detail.

Bonus Points:

• Basic understanding of data science concepts, including common machine learning models and statistical analysis.

• Experience with machine learning data preparation.

• Experience in the logistics or supply chain industry.

• Experience in a startup environment.