In the role of Data Engineer 2, we'll count on you to: • Build and maintain batch and streaming ingestion pipelines from source systems into the enterprise data platform. • Implement robust error handling, retries, and recovery patterns to improve reliability. • Collaborate with platform teams on connectivity, access, and operational readiness. • Develop transformation models using a layered structure (raw, conformed, curated) to separate concerns and improve maintainability. • Use dependency management patterns to ensure correct build order and reproducible transformations. • Leverage shared code and reusable components to standardize patterns, reduce duplication, and enforce consistent logic. • Implement incremental processing where appropriate to reduce runtime and warehouse cost. • Implement data quality tests (e.g., unique, not_null, accepted_values) on critical models and key business entities. • Configure and monitor source freshness expectations for important upstream datasets. • Maintain model and column-level documentation; publish documentation and lineage to enable impact analysis and change management. • Integrate transformations into CI/CD workflows (build, test, docs generation) before promotion to higher environments. • Participate in code reviews, follow branching standards, and adhere to quality gates and approval processes. • Support release and rollback practices to maintain stability and reduce risk. • Tune transformations for performance and efficiency; partner with stakeholders to manage cost impacts. • Support monitoring and alerting for pipelines and transformations; respond to incidents and perform root cause analysis. • Document operational procedures and contribute to runbooks/playbooks. Preferred Qualifications • Hands-on experience with dbt Cloud or dbt Core in Git-integrated workflows. • Experience with orchestration tools (e.g., Airflow, Dagster, Prefect) and scheduling patterns. • Experience implementing CI/CD quality gates for data (tests, linting, docs artifacts). Required Qualifications • Bachelor's degree in Computer Science, Information Systems, Data Engineering, or equivalent practical experience. • Minimum 3 years of experience in data engineering, data integration, or related roles. • Proven SQL proficiency and experience building transformations and pipelines. • Familiarity with cloud data platforms and modern ELT patterns. Qualifications