Servant leadership in a data engineering role

This post explores the concept of servant leadership in the context of government consulting as a tech worker, and reflects about Large Language Models (LLMs) as a productivity tool...

I recently applied for a position with a consulting company, Xentity Corp, and the concept of "servant leadership" was briefly mentioned by an interviewer.

After the interview I wanted to better understand what servant leadership means, so I asked Claude (3.5 Sonnet) for some book recommendations or online materials. I tried to get Claude to narrow down responses to government consulting and data engineering. I went back and forth with Claude. All of the recommendations were OK, but nothing was really jumping out at me for a book to read, or papers to download.

Then it occurred to me this is a LLM so why don't I ask it to summarize the material? This is what LLMs are great at: synthesizing and generating reviews of material in a digestible and understandable way. Here is an excerpt, just the end of conversation:

[Q] Have not been able to find a good book that I am interested in reading. Please summarize servant leadership in the context of government consulting- for a tech position (data engineer).

[A] I understand you haven't found a book that captures your interest. Let me summarize servant leadership in the context of government consulting for a tech position like a data engineer: Servant leadership in government consulting as a data engineer involves:

1. Prioritizing stakeholder needs:
   • Focus on understanding and meeting the needs of government clients and end-users.
   • Actively listen to requirements and pain points before proposing solutions.
2. Empowering team members and clients:
   • Share knowledge and skills to help government staff better understand data processes.
   • Mentor junior team members and client personnel in data engineering practices.
3. Ethical data handling:
   • Prioritize data privacy, security, and responsible use of public information.
   • Advocate for transparent data practices and clear communication of data limitations.
4. Long-term thinking:
   • Design scalable, maintainable data solutions that will serve the government beyond your contract.
   • Consider the long-term impact of data architecture decisions on future government operations.
5. Collaborative approach:
   • Work closely with other departments and stakeholders to ensure data solutions align with broader government goals.
   • Facilitate cross-functional communication to bridge gaps between technical and non-technical teams.
6. Continuous improvement:
   • Regularly seek feedback on data solutions and processes.
   • Stay updated on emerging technologies and best practices to better serve government clients.
7. Accountability and transparency:
   • Take responsibility for project outcomes and be open about challenges or limitations.
   • Provide clear, understandable explanations of complex data concepts to non-technical stakeholders.
8. Public service mindset:
   • Align data engineering efforts with the broader mission of improving government services and citizens' lives.
   • Consider the societal impact of data-driven decisions and advocate for equitable solutions.

In practice, this might involve:

• Developing user-friendly data dashboards for government decision-makers
• Creating efficient data pipelines that respect resource constraints of government systems
• Implementing data governance practices that ensure compliance with public sector regulations
• Conducting workshops to upskill government staff in data literacy and basic data engineering concepts

Would you like me to expand on any specific aspect of this summary?

Overall, this was a really good LLM interaction! It was genuinely helpful, and everything seemed legitimate in the response. In future posts I will share some LLM failure modes that I have seen or learned about. I am both fascinated and biased about LLMs, having studied Linguistic Anthropology as an undergraduate.

announcement

I start working at Xentity in October 2024 as a Senior Data Engineer contracted to U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS).