Role Hub
Data Career Resources
Decision impact frameworks and pipeline ownership maps for Data Engineers, Data Scientists, and Analytics roles.
When to use this
Do this first if:
- You're targeting Senior DE, DS, or Analytics roles
- Your resume reads like a technical spec instead of a business impact summary
- You've built pipelines or models but struggle to articulate their business value
Skip this if:
- You're looking for pure ML/AI research roles
- You're early-career without production data system ownership
- Your current materials are already getting callbacks
Data Role Signal Checklist
What hiring teams evaluate at senior levels
Show which business decisions your data work enabled and their outcomes.
Include data volumes, processing times, and reliability metrics.
Show how you worked with product, engineering, or business teams.
Quantify the business impact of your models or analyses (revenue, efficiency, accuracy).
Show how you improved data quality, freshness, or trustworthiness.
Example
Built data pipelines using Spark and Airflow to process customer data.
Designed customer segmentation pipeline processing 50M records daily, enabling personalized pricing that increased conversion by 12% ($3M annual impact).
The 'after' version connects technical work to business outcomes.
Evidence
Average time to first interview for data clients
Askia client dataAverage compensation increase for data role clients
Askia client outcomesMore callbacks with outcome-focused data resumes
Askia A/B testingFrequently asked questions
Should I focus on tools or outcomes on my resume?
Outcomes first. Mention tools as context, but lead with the business impact of your work.
How do I show Senior Data Engineer scope?
Quantify pipeline scale (records, latency), reliability (uptime, SLAs), and team impact (users, decisions enabled).
How do I describe ML model impact?
Lead with the business metric improved (revenue, efficiency, accuracy), then describe the model approach.
What's the difference between DE and DS positioning?
DE emphasizes infrastructure, scale, and reliability. DS emphasizes analysis, modeling, and decision support.
Do I need to know both Python and SQL?
For most senior data roles, yes. Demonstrate proficiency through impact examples, not tool lists.
Ready to land your next data role?
Book a strategy call and get personalized feedback on your positioning.
