[Hiring] Data Engineer @Outrider

Role Description

We are seeking a Data Engineer with strong Python and distributed processing skills to design, build, and maintain scalable data pipelines and the infrastructure behind our analytics products. This role focuses on delivering reliable, performant data systems by developing robust ETL/ELT workflows, optimizing data storage and processing, and ensuring data availability and quality at scale.

Key Responsibilities

• 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.

Qualifications

• 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.

Preferred Qualifications

• Experience with workflow orchestration tools (e.g., AWS Step Functions, Prefect, Dagster).
• Familiarity with modern data stack concepts: ELT patterns, data lakehouse architecture, semantic layers, and governance.
• Experience implementing automated data quality frameworks and pipeline observability.
• Exposure to streaming or near-real-time data processing (e.g., Kafka, Spark Streaming, Pub/Sub).

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.

Skills & Attributes

• 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.

Assignment

4-month contract with possibilities to extend.