Junior data engineers build pipelines. Seniors build trust

2024-04-11 · Source: DataExpert.io Newsletter · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

The role of data engineers is fundamentally shifting from merely building pipelines to acting as product owners for data, especially with AI automating much of the traditional grunt work. This evolution, highlighted in a series on data warehouse setup, emphasizes that data engineers must identify what data products will achieve market fit with their customers (coworkers) and then build them. The article stresses the importance of strategic thinking, product sense, and interpersonal skills over purely technical tasks like SQL writing or debugging. It argues that senior data engineers build trust by understanding customer needs, prioritizing high-impact use cases, and even "showboating" their work, rather than just focusing on technical perfection. Building trust involves being responsive, understanding existing "pillars of truth" (even if imperfect), and strategically spending credibility to advocate for better metrics and infrastructure.

Key takeaway

For Data Engineers aiming for senior roles, recognize that your primary function is evolving beyond pipeline construction to data product ownership. You must cultivate trust with your internal customers by understanding their critical data needs and existing "pillars of truth," even if imperfect. Prioritize solutions that drive business impact and strategically communicate your work to gain buy-in for architectural improvements, ensuring your data is not just trustworthy but actively trusted and utilized.

Key insights

Data engineers must evolve into data product owners, prioritizing trust and strategic impact over mere pipeline construction.

Principles

• Data engineers own data products.
• Trust in data requires trust in the engineer.
• Prioritize existing customer data surfaces.

Method

Understand data, customers, and key use cases. Prioritize, deliver, and actively market solutions to build trust and ensure impact. This involves iterative improvement and strategic "showboating."

In practice

• Rename tables for broader company utility.
• Maintain high availability for key stakeholders.
• Integrate with existing, trusted data surfaces.

Topics

• Data Engineering
• Data Product Management
• AI in Data Engineering
• Data Trust & Credibility
• Stakeholder Engagement
• Data Strategy

Best for: CTO, VP of Engineering/Data, Executive, Data Engineer, Director of AI/ML, AI Product Manager

Related on AIssential

• The AI Illusion: Why Data Engineers Will Be More Important Than Ever — AI's utility is directly proportional to the quality and governance of its underlying data.
• The Next Step for Data Analysts Isn’t More Tools — It’s Understanding the Data Pipeline — Modern data analysts must understand the entire data pipeline, not just the final output, to increase their value.
• I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. — True data engineering extends beyond scripting to build reliable systems through idempotency, persistence, and scheduling.
• How Agoda Built a Single Source of Truth for Financial Data — Consolidating disparate data pipelines into a unified system enhances data consistency and operational efficiency.
• Building Agent Ready Data Pipelines From Scratch — AI agents require data pipelines to provide rich metadata for accurate interpretation, beyond traditional data quality.
• Why Healthcare AI Needs Data Engineers More Than Ever — Healthcare AI's primary deployment bottleneck is data infrastructure, not model capability, demanding specialized data engineering.

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Editorial summary, takeaway, and curation by AIssential. Original article published by DataExpert.io Newsletter.