Senior Data Engineer – Competencies
1. Cognitive Ability (30%)
- Demonstrates strong deductive reasoning and structured problem-solving, breaking down complex and ambiguous challenges into clear hypotheses and logical conclusions.
- Balances short-term tactical needs with long-term scalability by evaluating tradeoffs in real time.
- Integrates diverse data and business inputs into coherent models that enable better decisions.
- Assesses alternative architectures, tools, or approaches and selects the path that maximizes enterprise value.
2. Execution Orientation (25%)
- Translates complex requirements into production-ready data pipelines, ensuring solutions move from concept to reality.
- Maintains accountability for delivery in fast-paced, M&A-driven environments, persisting through ambiguity and shifting priorities.
3. Attention to Details (20%)
- Applies precision in relational and non-relational database design, ensuring schemas are optimized for both reliability and performance.
- Designs and adapts ETL/ELT pipelines with a disciplined approach, selecting and applying modern tools (e.g., Airflow, dbt, Python) while upholding exacting quality standards.
- Establishes rigorous validation and monitoring practices to safeguard accuracy, consistency, and trustworthiness across complex data flows.
4. System Thinking (15%)
- Recognizes interdependencies across ERP, finance, and operational systems, integrating them into unified models that enable scale and long-term resilience.
- Envisions and designs analytics-ready data architectures that serve diverse use cases, from real-time operations to enterprise reporting.
- Applies governance and metadata practices not as compliance exercises, but as mechanisms to keep systems coherent, secure, and adaptable as the business evolves.
5. Empathy & Collaboration (10%)
- Builds alignment across engineering, finance, and operations by translating technical concepts into clear, accessible language.
- Guides teams and vendors with a collaborative approach, creating trust and accountability while maintaining technical rigor.
- Balances advocacy for best-practice architecture with empathy for business constraints, enabling pragmatic solutions that teams can adopt and sustain.
- Establishes and enforces validation, quality, and monitoring standards so that systems remain dependable at scale.
