Python Data Engineer
About the role
- Collaborative partner who can patiently communicate at the appropriate level to both business and technology teams to understand business needs and pain points
- Creative in meeting the client's core needs with their technology
- Determined and able to manage obstacles while maintaining a positive outlook
- Self-driven lifelong learner passionate about learning new data tools and best practices
- 5+ years of data engineering experience
- Strong experience designing and developing ETL and data pipelines with Python
- Experience working with AWS Data Analytics stack: Amazon Athena, AWS Glue, etc.
- Experience working with businesses to understand the appropriate data model (relational, tabular, transactional) for their data solution
- Understanding of data modeling (such as Kimball, Inman, Data Vault design approaches)
- Excellent foundation of consulting skills: analytical, written and verbal communication, and presentation skills
- Demonstrated ability to identify business and technical impacts of user requirements and incorporate them into the project schedule
- Deep experience designing and building ELT jobs to move and transform data from various source types and performing exploratory data analysis, data cleansing, and aggregation
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
