Data Engineer, Outrider

• Experience: 3+ years of professional experience in data engineering
• PostgreSQL: Schema design, indexing strategies, query optimization, and performance tuning
• Python: Pipeline development, data validation, and orchestration frameworks
• Distributed Processing and Storage: Hands-on production experience with tools such as AWS Athena, Apache Spark, etc
• ETL/ELT: Proven experience designing and implementing pipelines in production
• Cloud: AWS (S3, EKS, Glue, Athena)
• Data Modeling: Dimensional modeling, data warehousing patterns, and reproducible transformations
• Engineering Practices: Git workflows, code reviews, testing, and CI/CD
• LLM AI Agents: ability to use agents effectively to increase output
• Systems-level thinking with strong attention to correctness, reliability, and scale
• Ability to balance delivery speed with data quality, maintainability, and cost efficiency
• Clear written and verbal communication with both technical and non-technical partners
• Comfortable owning end-to-end data products from architecture through production support

Desirable

• Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and deliver data across the organization
• Develop and optimize distributed data processing jobs using Python for large-scale data transformation and aggregation
• Architect and manage PostgreSQL schemas, tables, indexes, and query performance to support downstream analytics and reporting
• Build and maintain Python-based data workflows to orchestrate, validate, and deliver data reliably across environments
• Monitor and improve data quality, freshness, and completeness through automated checks, alerting, and observability tooling
• Design and manage cloud-based data infrastructure on AWS
• Partner with data analysts and stakeholders to translate requirements into well-modeled, maintainable data products
• Maintain documentation for pipelines, data models, data lineage, and infrastructure
• Troubleshoot pipeline failures and data issues, providing timely root-cause analysis and remediation
• What Success Looks Like;
• Reliable pipelines. Pipelines are performant and trusted by downstream consumers
• Maintainable infrastructure. Data systems are well-documented, cost-efficient, and easy to extend
• Proactive quality. Data issues are detected early, with clear ownership and fast resolution paths
• Consistent delivery. Data is correctly modeled and delivered on time across all environments
• 4-month contract with possibilities to extend