Staff Data Engineer

This is a fully remote position, open to applicants in Mexico.

📋 Description

• Oversee the creation and execution of shared, reusable data models, specifying common fact tables, conformed dimensions, and a semantic/metrics layer that acts as the definitive source of truth for analytics operations.

• Propel the standardization of data engineering methodologies across ADE and functional analytics teams, which includes pipeline patterns, CI/CD workflows, naming conventions, and data modeling benchmarks.

• Collaborate with Data Infrastructure to enhance orchestration, refine pipeline decomposition, and set up secure development/testing environments with access to production data.

• Design and implement a proactive data governance strategy, partnering with upstream data producers to establish data contracts, service level objectives (SLOs), and code-enforced quality gates that identify issues prior to production.

• Work alongside Data Science leaders and Product Management to convert metric definitions into dependable, certified data pipelines that support executive dashboards, weekly business review (WBR) reporting, and growth assessment.

• Alleviate operational strain by enhancing pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that ensure on-call duties are manageable.

• Assess and incorporate AI-native tools into the data development cycle, facilitating conversational data exploration with safeguards and AI-aided pipeline development.

⛳️ Requirements

• Bachelor’s degree in Computer Science or a related technical field, or equivalent technical experience.

• 12+ years of experience in data engineering or analytics engineering, demonstrating increasing scope and technical leadership.

• 12+ years of SQL experience, including complex analytical queries, window functions, and performance optimization at scale (Spark SQL).

• 8+ years of proficiency in Python development, focusing on the construction and maintenance of production data pipelines.

• Extensive knowledge in dimensional data modeling, schema design, and scalable data architecture, with practical experience in developing shared data models across various business domains.

• Strong familiarity with orchestration tools (Airflow is strongly preferred) and dbt, encompassing pipeline design, scheduling techniques, and failure recovery strategies.

• Proven ability to foster cross-team technical alignment, setting standards, influencing without direct authority, and collaborating across the boundaries of Data Engineering, Data Science, Data Infrastructure, and Product Engineering.

🏝️ Benefits

• Health insurance

• Retirement plans

• Paid time off

• Flexible work arrangements

• Professional development