Senior Manager - Data Engineering

Team: Data Engineering

Location: Remote

Commitment: Full-Time

Workplace Type: remote

Salary:

Key Responsibilities:

Snowflake Platform

• Serve as the internal authority on Snowflake architecture, performance tuning, cost governance, and security (RBAC, data masking, network policies).
• Design and maintain a scalable, well-documented warehouse structure including database, schema, and object hierarchy standards.
• Drive Snowflake feature adoption — dynamic tables, Snowpark, data sharing, and emerging capabilities.

dbt & Transformation Layer

• Own the dbt project end-to-end: modeling conventions, testing strategy, documentation standards, and CI/CD integration.
• Establish and enforce a layered modeling approach (staging → intermediate → marts) that downstream teams can trust and self-serve.

Azure Data Ecosystem

• Lead the design and operation of data pipelines on Azure, including Azure Data Factory
• Ensure reliable, monitored data movement from source systems into Snowflake with clear SLAs and alerting.

Team Leadership

• Manage, mentor, and grow a team of data engineers — running regular 1:1s, setting performance goals, and building a culture of engineering excellence.
• Own hiring, onboarding, and career development for the data engineering function.
• Translate business requirements from stakeholders into well-scoped, prioritized engineering work.

Standards & Governance

• Define and enforce organization-wide ETL/ELT best practices, naming conventions, and code review standards.
• Champion data quality, observability (e.g., dbt tests), and lineage across the platform.
• Proactively identify opportunities for data process improvements and lead initiatives to implement these changes.

Qualifications:

Bachelor’s degree in computer science, Information Technology, Engineering, or a related field

7+ years in data engineering, with at least 2 years in a team lead or management role

Deep, production-grade Snowflake expertise — you have designed warehouse architectures, optimized query performance, managed costs, and implemented enterprise security controls

Fluency with dbt: you have built and maintained dbt projects at scale and can articulate opinionated best practices

Hands-on Azure Data Factory experience

Strong SQL skills and proficiency in Python for data pipeline development and automation

Proven ability to lead and grow a small team while remaining technically engaged

Strong communicator who can translate complex data concepts to non-technical stakeholders and contribute to strategic planning conversations

Nice to have:

Familiarity with data observability tooling (Elementary, Monte Carlo, or similar).

Exposure to Snowflake Cortex, Snowpark ML, or other AI/ML capabilities on Snowflake

Experience in a high-growth or scale-up environment where standards were built from the ground up