Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and sustain scalable and dependable data pipelines utilizing modern data engineering tools and technologies.

• Contribute to the re-architecture of our data warehouse to enhance performance, scalability, and data quality.

• Execute data cleansing, transformation, and validation processes to guarantee data accuracy and consistency.

• Collaborate with fellow engineers and stakeholders to define data requirements and create data models.

• Construct and manage data infrastructure on GCP, which includes data lakes, data warehouses, and data pipelines.

• Optimize data storage and retrieval for enhanced performance and cost-effectiveness.

• Monitor the performance of data pipelines and address any issues that arise.

• Implement best practices for data security and governance.

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

• Facilitate data access for machine learning algorithms and tools.

• Support the AI team with basic data analysis and reporting tasks.

• Collaborate with the engineering team to assist with machine learning models.

• Offer technical guidance and mentorship to the data team.

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

• Work alongside cross-functional teams to integrate machine learning solutions into the Stord platform.

• Advocate for data democratization and encourage data-driven decision-making.

⛳️ Requirements

• Over 5 years of experience in data engineering or a related discipline.

• Demonstrated experience in building and maintaining data pipelines and data warehouses.

• Proficiency with cloud platforms, ideally GCP.

• Experience with SQL and data modeling.

• Familiarity with data transformation tools such as dbt or similar.

• Strong expertise in SQL and Python.

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

• Knowledge of data warehousing technologies (e.g., BigQuery, Snowflake).

• Understanding of data lake concepts and technologies.

• Awareness of data engineering best practices.

• Familiarity with basic machine learning concepts, data preparation techniques, and model evaluation.

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

🏝️ Benefits

• Health insurance.

• Retirement plans.

• Paid time off.

• Flexible work arrangements.

• Professional development opportunities.

• Bonuses.

• Stock options.

• Equipment allowances.

• Wellness programs.