The Data Engineer 2 designs, builds, and maintains reliable data pipelines and storage solutions that enable analytics and reporting. This role develops scalable batch and streaming processes, optimizes data models, and ensures data quality, security, and performance across environments. The position collaborates with analysts, data scientists, and software teams to deliver well-documented, testable, and production-ready data assets. The engineer contributes to standards, monitors pipelines, and supports incident resolution to meet service and compliance expectations.
Essential Job Skills/Duties
- Design and implement scalable data pipelines.
- Develop and optimize data models.
- Automate ingestion, transformation, and validation.
- Monitor, troubleshoot, and resolve pipeline issues.
- Document datasets, lineage, and standards.
- Collaborate with analytics and product teams.
Required Technical Skills
- Data Engineering Programming: Designing and maintaining scalable data processing systems using modern programming languages and frameworks, following engineering best practices.
- Data Engineering Programming: Builds and maintains defined data pipeline components using standard frameworks.
- Pipeline Ownership: Owns small batch or streaming pipelines end-to-end, ensuring stability, monitoring, and documentation.
- Data Modeling: Designs structured, scalable schemas aligned to defined analytics and reporting needs.
- Ingestion Into Platform: Implements reliable ingestion pipelines for structured and semi-structured data sources.
- Production Reliability: Monitors pipeline health, responds to alerts, and addresses defined reliability issues.
- Scale & Performance Tuning: Optimizes queries and pipeline components for efficiency and cost-effectiveness.
- Data Product Enablement: Prepares structured, well-documented datasets for defined analytical and reporting use cases.
Required Soft/Leadership Skills
- Clear, concise technical communication.
- Collaborative, cross-functional teamwork.
- Ownership of deliverables and timelines.
- Structured problem solving and prioritization.
- Continuous learning and adaptability.
Required Education & Experience
- Bachelor's degree in STEM field.
- Three or more years in data engineering.
- Experience building production data pipelines.
- Experience with cloud and orchestration tools.
Preferred Education & Experience
- Master's degree in data-related field.
- Experience with lakehouse architectures.
- Experience supporting data science workflows.
Supervisory Responsibilities
- No direct reports.
- Mentors peers on standards and practices.
