Storable is on a mission to power the future of storage by helping businesses manage and grow their self-storage operations. They are seeking a Data Engineer to support the design, development, and maintenance of data pipelines and infrastructure, ensuring data is reliable and accessible for reporting and analytics.
Responsibilities:
- Assist in building, maintaining, and optimizing scalable data pipelines
- Support ingestion, transformation, and validation of structured and semi-structured data
- Help monitor and troubleshoot pipeline performance issues
- Contribute to developing and maintaining ETL/ELT workflows
- Write clean, maintainable transformation logic in SQL and/or Python
- Ensure data is delivered accurately and on time to downstream systems
- Support workflow scheduling and monitoring using Apache Airflow
- Troubleshoot task failures and assist in improving workflow reliability
- Work with distributed processing frameworks such as Apache Spark
- Support querying and data access using tools like Trino (Presto) and AWS services
- Help implement data validation checks and monitoring
- Support data documentation and adherence to governance best practices
- Assist in maintaining consistency across datasets
- Partner with analytics, product, and engineering teams to understand data needs
- Translate business requirements into technical data solutions with guidance
- Contribute to improving data accessibility and usability
- Work within AWS-based data environments (S3, Glue, Athena, Redshift, etc.)
- Support performance tuning and cost optimization efforts
- Follow best practices in data modeling and schema design
Requirements:
- 1–3 years of experience in data engineering, analytics engineering, or related field
- Experience building or supporting data pipelines in cloud environments
- Proficiency in SQL
- Experience with Python or similar language for data processing
- Familiarity with Apache Airflow (or other orchestration tools)
- Exposure to AWS services such as S3, Redshift, Glue, Athena, or Lambda
- Basic understanding of data modeling and warehousing concepts
- Understanding of ETL/ELT processes
- Familiarity with data quality principles
- Exposure to schema design and analytical datasets
- Strong problem-solving mindset
- Clear written and verbal communication skills
- Eagerness to learn and grow within a collaborative environment
- Ability to take feedback and iterate quickly
- Exposure to distributed data processing tools (Spark preferred)
- Exposure to Apache Iceberg or modern table formats
- Experience working with BI tools such as Looker or Tableau
- Experience in a SaaS or product-led environment