Analytics Engineer
Impact: Strategic
Optimize data models, build data pipelines, and ensure data quality and accessibility for analytical purposes by bridging the gap between data scientists and data engineers. Utilize SQL, Python, and data warehousing technologies to transform raw data into actionable insights.
What the day looks like
- People interaction
- Moderate
- Team vs solo
- Team-oriented with significant solo work
- Client facing
- Never
- Impact visibility
- High
- Travel
- Low
- Schedule flexibility
- Moderate
- Remote work
- Mostly Remote
- Typical work hours
- 40-50 hours
- Stress level
- High
At a glance
- Median salary
- $105,000
- Entry-level
- $75,000
- Senior
- $140,000
- Growth by 2033
- 18%
- Demand
- Growing
- Freelance potential
- Low
- Salary growth potential
- Excellent
- Typical student debt
- $30,000 - $60,000
Skills you'll use
Hard skills
- SQL
- Python
- Data Warehousing
- ETL
- Data Modeling
- Cloud Platforms (AWS
- GCP
- Azure)
Soft skills
- Problem-solving
- Communication
- Attention to Detail
- Critical Thinking
- Adaptability
Technical complexity: High
How to get there
- Minimum education
- Bachelor's Degree
- Licensing
- No
- Years to mid-career
- 4
- Years to senior
- 8
- Career switching
- Moderate
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- Junior Analytics Engineer
- Analytics Engineer
- Senior Analytics Engineer
- Lead Analytics Engineer
- Data Architect
Future outlook
- Automation probability
- Low
- AI disruption risk
- Moderate
- Demand trend
- Growing
How people feel about it
- Overall satisfaction
- 4/10
- Meaning
- 4/10
- Work-life balance
- 3.5/10
- Prestige
- 7.5/10
- Social perception
- High