# Jobgether — Director, Automation Engineering

## Posting metadata

- Canonical JD URL: https://www.linkedin.com/jobs/view/4453583283
- Provider: LinkedIn public job posting
- Location/source location: United States
- Work model: Fully remote, United States
- Compensation: $165,000-$210,000 base plus annual bonus and long-term incentives
- Travel: Not disclosed
- Posted: Unavailable
- LinkedIn relative posting time at capture: 2 days ago
- Applicants: 28 applicants
- Generated: 2026-08-16 10:23 AM EDT
- Elapsed since posting: Unavailable

## Fit decision

- Positioning track: Technical manager
- Central mandate: Lead engineers building production Python libraries, AI agents, reusable automation workflows, evaluation systems, and data-platform components.
- Fit outcome: PASS
- Weighted fit score: 84.3%
- Keyword diagnostic: 20/20 (100.0%); missing: none
- Validation: PASS: content, positioning, word count, PDF page count, text extraction, publication, and public URL checks completed.

## Hard and material gaps

- Keith lacks direct advertising-technology, audience activation, attribution, incrementality, and data clean-room experience; those areas are preferred rather than the core engineering mandate.
- His cloud-data experience is AWS-heavy rather than five years specifically on Snowflake or Databricks.

## Evidence map

| Requirement | Weight | Evidence score | Keith evidence |
|---|---:|---:|---|
| Production Python libraries, services, testing, CI/CD, and architecture | 3 | 3/3 | Direct Python, API, SaaS, testing, deployment, and production-platform evidence. |
| LLM applications, RAG, vectors, agents, LangChain, and LangGraph | 3 | 3/3 | Direct current hands-on production experience. |
| Five years on cloud data platforms | 3 | 2/3 | Long-standing AWS data-platform experience; Snowflake/Databricks-specific duration is not established. |
| Evaluation, guardrails, regression testing, monitoring, and observability | 2 | 3/3 | Direct evaluation, observability, model monitoring, governance, and quality-control evidence. |
| Reusable components and data-collaboration workflows | 2 | 2/3 | Strong reusable platform and workflow evidence; no direct clean-room implementation. |
| Hire, mentor, and lead software and AI engineers | 2 | 3/3 | Built and led multiple distributed engineering and AI teams. |
| Advertising technology, attribution, and data clean rooms | 1 | 0/3 | No direct source-supported experience; these are preferred/domain-plus items. |
| Fully remote distributed collaboration | 1 | 3/3 | Substantial remote and distributed-team leadership. |

## Artifact metadata

- Resume URL: https://bit.ly/4icgHqH
- Cover letter URL: https://bit.ly/3RRY5lh
- Resume PDF: 2 pages, 292025 bytes
- Cover PDF: 1 page, 20772 bytes

## Full normalized job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Automation Engineering based in United States.
This is a senior engineering leadership opportunity focused on building AI-enabled automation systems and production-grade software for a large-scale data collaboration ecosystem. You will set technical direction across AI agents, automation workflows, reusable software components, and data platforms. The role combines hands-on technical leadership with team development, mentorship, and operational ownership. You will help transform complex audience activation, measurement, and reporting workflows into scalable, repeatable solutions. You’ll work across engineering, product, operations, and data teams to move emerging AI capabilities from prototypes into reliable production systems. The environment emphasizes engineering excellence, experimentation, observability, and continuous improvement. This is a fully remote role with significant influence over the next generation of intelligent automation capabilities.
Accountabilities
Lead the design, development, and delivery of AI agents and automation workflows using technologies such as Snowflake Cortex, LangChain, LangGraph, or comparable frameworks.
Establish reusable tools, APIs, libraries, and components that enable engineers to build and extend agentic workflows efficiently.
Guide retrieval-augmented generation, context management, prompt engineering, tool-use, validation, and human-in-the-loop patterns to improve AI reliability, accuracy, latency, and cost efficiency.
Establish evaluation frameworks, regression testing, monitoring, and performance metrics for AI-agent behavior, including task completion, groundedness, accuracy, latency, cost, and failure rates.
Define appropriate guardrails and validation mechanisms to minimize hallucinations, unsafe outputs, and unreliable automation.
Oversee the development and maintenance of production-grade Python applications, libraries, and services, championing modular architecture, object-oriented design, automated testing, CI/CD, code reviews, observability, and documentation.
Develop reusable software patterns that reduce bespoke engineering effort and enable consistent solutions across partner engagements.
Translate recurring business and operational requirements into scalable technical solutions in collaboration with product, engineering, operations, and data platform teams.
Lead the development of reusable Python libraries supporting data clean room capabilities and audience and measurement workflows, including audience onboarding, ingestion, indexing, activation, campaign analysis, reach and frequency, attribution, and incrementality.
Turn complex analytical and data collaboration processes into configurable, self-service components that can be deployed efficiently across teams and partners.
Hire, develop, mentor, and retain a high-performing team of software and AI engineers while establishing standards for agentic systems, AI-assisted development, reusable libraries, and production automation.
Promote a culture centered on reliability, maintainability, technical excellence, operational discipline, and continuous improvement.
Requirements
Bachelor’s degree or equivalent practical experience in Computer Science, Information Systems, Software Engineering, Electrical Engineering, Electronics Engineering, or a related technical discipline.
8+ years of software engineering experience, including experience leading, mentoring, or managing engineering teams.
Deep hands-on experience developing production-grade software in Python, including libraries, services, testing, CI/CD, code reviews, and maintainable system architecture.
Practical experience building LLM-powered applications involving RAG, vector databases, prompt engineering, or frameworks such as Snowflake Cortex, LangChain, LangGraph, or similar technologies.
Experience designing AI-enabled workflows, reasoning agents, tool-using agents, or sophisticated automation systems.
5+ years of hands-on experience with cloud data platforms such as Snowflake, Databricks, or comparable technologies.
Strong understanding of production system design, including scalability, reliability, observability, performance optimization, monitoring, and operational support.
Ability to provide technical direction while balancing innovation with production reliability, security, maintainability, and business priorities.
Strong leadership and communication skills, with the ability to mentor engineers, facilitate technical design discussions, and collaborate effectively across product, engineering, operations, and data teams.
Exposure to data clean room concepts or platforms such as Snowflake Clean Rooms, Databricks Clean Rooms, LiveRamp, or Habu is preferred.
Experience with advertising technology, audience activation, campaign delivery, reach and frequency, attribution, incrementality, measurement, or reporting workflows is a plus.
Snowflake SnowPro Core, Databricks Certified Data Engineer Associate, or a comparable cloud/data platform certification is preferred.
Strong problem-solving skills and an ability to turn complex technical or operational challenges into reusable, scalable solutions.
Willingness to operate in a fully remote environment while maintaining strong collaboration and accountability across distributed teams.
Benefits
Salary: $165,000–$210,000 per year.
Eligibility for annual bonus and long-term incentive compensation.
Fully remote work arrangement within the United States.
Company-sponsored medical, dental, and vision insurance.
401(k) retirement benefits.
Paid leave and other time-off benefits.
Tuition reimbursement and opportunities to support continued professional development.
Access to a variety of employee discounts, perks, and additional benefits.
Reasonable workplace accommodations are available for qualified individuals with disabilities and disabled veterans.
How Jobgether Works
We use an
AI-powered matching process
to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice:
By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.