Data Engineer
About The Position
CerebriOS is a software company building B2B SaaS products that help businesses make better decisions, streamline operations, and get more value from their data. Our products combine practical business workflows with intuitive technology designed for everyday use. You will join the engineering team behind our new mid-market analytics platform, building the data foundations that power reporting, dashboards, and product insights. You will work across data pipelines, integrations, and storage, helping turn data from different sources into reliable, well-structured information that customers and internal teams can use with confidence.
Requirements
- 3+ years of professional experience in data engineering or a closely related role
- Strong SQL skills and experience working with relational databases
- Hands-on experience building and maintaining data pipelines
- Experience with Python or another language commonly used for data engineering
- Understanding of data modeling, ETL/ELT processes, and data quality practices
- Experience working with cloud-based data infrastructure
- Strong troubleshooting and analytical skills
- Ability to explain technical decisions clearly and collaborate with engineers, analysts, and product stakeholders
- Comfortable working independently in a remote, cross-functional environment
Nice To Haves
- Experience with AWS or GCP
- dbt, Airflow, Dagster, or similar data tooling
- Experience with modern data warehouses such as Snowflake, BigQuery, or Redshift
- Experience working with APIs and third-party data integrations
- Familiarity with analytics, reporting, or business intelligence products
- Previous B2B SaaS experience
- Experience working with large or rapidly changing datasets
Responsibilities
- Design, build, and maintain reliable data pipelines for the analytics platform
- Develop data ingestion and transformation processes for multiple data sources
- Work with backend engineers and analysts to define data structures that support reporting and analytics use cases
- Improve data quality, consistency, and reliability across the platform
- Build and optimize SQL-based data transformations and workflows
- Monitor data pipelines and investigate failures, performance issues, and unexpected data behavior
- Contribute to data architecture and help establish scalable practices as the platform grows
- Document data models, pipelines, and important business logic so others can work confidently with the data
- Collaborate with product and engineering teams to understand how data should support new product capabilities
Benefits
- professional development opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
