Overview

At Brooklyn Data, Senior Analytics Engineers lead the design and implementation of scalable data models and pipelines that turn complex data into clear, trustworthy insights. They work closely with functional leadership and data solutions partners to shape data architectures and modeling strategies that align with client goals and deliver measurable impact. Because our clients are mostly US-based organizations, we look for the ability to communicate with professional proficiency in English, verbally and in writing.

Responsibilities

Engineering Leadership

• Lead complex technical implementations, shaping data architecture and transformation pipelines that prioritize performance, maintainability, and reusability.
• Drive adoption of best practices for dbt development, testing, and CI/CD while mentoring peers through code reviews, pairing, and technical guidance.
• Contribute to internal tooling, shared dbt packages, and scalable frameworks that raise the standard of analytics engineering across teams and clients.

Cross-Team Collaboration

• Partner with analysts, engineers, and stakeholders to translate complex and often ambiguous business requirements into well-structured data models and semantic layers.
• Serve as a technical and strategic advisor in cross-functional planning—balancing stakeholder requirements, data governance, and long-term scalability.
• Model strong communication and mentorship, helping other AEs and cross-functional teammates develop clarity and confidence in data architecture decisions.

Project Enablement

• Lead end-to-end delivery of analytics engineering workstreams, ensuring solutions are reliable, well-documented, and aligned with client objectives.
• Anticipate and address systemic data quality or modeling issues, implementing durable solutions that improve trust and efficiency across the stack.
• Enable sustainable project delivery by optimizing performance, reducing technical debt, and contributing to shared standards that scale across teams.

Skills & Qualifications

• Programming languages (e.g., SQL, Python)
• Business intelligence platforms (e.g., PowerBI, Sigma)
• Expert proficiency in dbt is required.
• Expert proficiency in data modeling approaches and philosophies (e.g., Kimball, OBT)
• Expert proficiency with at least one cloud data warehouse (e.g., Snowflake, BigQuery)
• Expert proficiency with version control and git.
• Knowledge of common data integration patterns (e.g., CDC, ELT, etc.)
• Knowledge of common data integration / orchestration platforms (e.g., Fivetran, Dagster, Apache Airflow)
• Ability to communicate with professional proficiency in English, verbally and in writing.

Bonus points for:

• Strong analytical instincts—you can spot patterns, anomalies, and relationships across large, complex datasets.
• Experience working with event-driven data pipelines or pub/sub architectures to move and process data efficiently across systems.
• Experience using AI or automation tools to enhance analytics engineering workflows—whether for testing, documentation, or performance optimization.

Physical Requirements

• Frequent sitting at a desk performing work on a computer
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions

Compensation Range: $135,000 - $150,000 annually

Please note that compensation packages are finalized after the interview process is concluded. We use a competency-based approach to base pay, which means it is based on the competencies and skills demonstrated for this role.