Director of Data Engineering - Outpost

Location: Remote

Working Hours: US East Coast hours (typically 9:00 AM to 5:00 PM Eastern Time)

About The Company

A fast-growing, mission-driven organization partnering with clients to explore, design, and implement AI strategies that are secure, scalable, and human-centered. We believe AI should amplify human potential, not replace it—and we build with that conviction in every engagement. From advisory and tooling to implementation and education, we meet clients where they are and help them integrate AI in ways that align with their mission and values.

Our team is composed of doers and leaders who roll up their sleeves, work collaboratively, and value feedback and excellence over titles. We thrive on creativity, accountability, and delivering outcomes that are both beautiful and practical. At Human Agency, we lead with purpose, love, and adventure—doing meaningful work with people we care about and making the journey one worth taking.

Position Overview

We are seeking an experienced Director of Data Engineering to stabilize and grow modern data platforms and lead AI-enabled outcomes across multiple client engagements. You will rotate across projects—sometimes coordinating a client’s data function to ensure continuity and accelerate delivery, and other times leading hands-on engineering, reliability work, or AI implementation. This role combines technical leadership with consulting excellence, pairing deep data expertise with clear executive communication.

Key Responsibilities

Engagement Leadership

• Coordinate across data engineering, analytics, and data science leads to manage priorities, releases, and delivery rhythms.
• Map ownership and dependencies, reduce single points of failure, and maintain service catalogs and decision logs.
• Lead transition planning and knowledge transfer with internal teams and vendors while sustaining delivery.
  
  

Platform & Pipeline Ownership

• Build, operate, and optimize ELT/ETL pipelines across batch and streaming sources.
• Manage orchestration (Airflow), transformations (dbt or equivalent), environments, and CI/CD for analytics code.
• Optimize warehouse performance (e.g., Snowflake) and cost efficiency.
• Rapidly assess and stabilize existing pipelines, data contracts, dependencies, and SLAs/SLOs.
  
  

Data Reliability & Governance

• Implement monitoring, alerting, data quality checks, and SLO-based reliability practices.
• Maintain lineage visibility, metadata management, and access controls.
• Participate in incident response rotations and maintain runbooks, postmortems, and change management documentation.
  
  

Analytics Enablement

• Partner with analysts and business stakeholders to deliver trusted datasets and semantic models.
• Support BI tools (Looker, Power BI, Tableau) and maintain documented, versioned data sources of truth.
  
  

Client Collaboration & Consulting

• Translate business needs into technical data solutions and actionable options with clear trade-offs.
• Facilitate discovery and alignment sessions, managing scope, risks, and stakeholder expectations.
• Prepare concise, executive-ready updates, metrics, and recommendations.
  
  

Documentation & Communication

• Produce clear technical documentation, decision logs, and release notes. Communicate technical trade-offs effectively to non-technical stakeholders.
• Establish lightweight governance frameworks for data quality, access, lineage, and version control.
• Lead structured knowledge transfer sessions and create handover materials.
  
  

Qualifications

Required

• 7+ years of experience in data engineering or analytics engineering, with ownership of production pipelines and BI at scale.
• Proven success stabilizing and maintaining production data platforms and critical pipelines.
• Expertise in modern data platforms (e.g., Snowflake), orchestration (Airflow), and transformation frameworks (dbt or equivalent).
• Strong skills in ELT/ETL design, APIs, SQL optimization, and cloud data storage.
• Hands-on experience with data reliability, observability, lineage, and testing.
• Demonstrated ability to operate effectively in ambiguous or partially documented environments.
• Excellent written and verbal communication, with experience presenting to clients or executives.
  
  

Preferred

• Interest or experience in Generative AI and Machine Learning.
• Basic Python scripting skills for data and LLM workflows.
• Practical experience building end-to-end AI workflows (e.g., RAG pipelines, retrieval, and evaluation).
• Understanding of LLM behavior, tuning, and performance optimization.
• Familiarity with BI tools, semantic layer design, and vector databases.
• Experience with MLOps concepts for LLMs, including versioning, evaluation, and monitoring.
• Awareness of data security, governance, and access management best practices.
  
  

Mindset & Traits

• Systems thinker with strong analytical and problem-solving abilities.
• Clear communicator who can balance technical precision with strategic context.
• Adaptable, collaborative, and driven to build scalable, reliable data systems.