Lead Data Engineer

Lead Data Engineer

$100000 - $145000 per year | Ciudad de México, CDMX | Hybrid | Permanent

Exciting Lead Data Engineer Opportunity with Thriving HealthTech Organization!

A bit about us:

We are an industry leader in the HealthTech space, but much more than that. We have built a customizable platform for health and wellness professionals to allow them provide the best care and treatment for their passions from start to finish. To keep it simple, our platform helps clinicians run and grow the practices with ease of service so they can concentrate on the most important thing... patients!

Why join us?

• Competitive Base Salary plus Bonus!
• Best in class healthcare, dental, vision, benefits across the board!
• Hybrid Remote!
• Flexible Time Off in addition to 15 paid holidays!
• Accelerated Career Growth!
• Extremely talented engineering leadership team to partner with on your path to further your career!
• Tackle challenging engineering tasks with state of the art new technology!
• Home Office and Internet Stipends!
• Catered Lunch!

Job Details

We are seeking a highly motivated and experienced Lead Data Engineer to join our dynamic and fast-paced team in the HealthTech industry. This is a fantastic opportunity for an individual with a strong technical background and leadership skills to contribute to our data strategy and drive our data initiatives forward. The successful candidate will be responsible for leading projects around the optimization of our data architecture, as well as developing and implementing data models and ETL processes. You will have the chance to work with a variety of technologies such as DBT, AWS, Airflow, Prefect, Airbyte, Fivetran, Kafka streaming, Snowflake, and Kinesis.

1. 1. Leading the design and implementation of scalable data models and ETL processes.
2. 2. Developing and maintaining an efficient data pipeline architecture.
3. 3. Collaborating with the data science team to build optimal data systems and pipeline architecture.
4. 4. Implementing data flow and collection for cross-functional teams.
5. 5. Working with stakeholders including the executive, product, data, and design teams to assist with data-related technical issues and support their data infrastructure needs.
6. 6. Designing and implementing data schemas and models that can scale and perform well.
7. 7. Leveraging your experience with cloud platforms, specifically AWS, to manage and optimize our data architecture.
8. 8. Providing leadership and mentoring to junior data engineers.
9. 9. Ensuring data quality and implementing data governance in collaboration with the data science team.

Qualifications

1. 1. A minimum of 7 years of experience in a Data Engineer role.
2. 2. Bachelor's degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field. Advanced degree is a plus.
3. 3. Excellent SQL scripting skills and proficiency with Python.
4. 4. Experience with big data tools: Hadoop, Spark, Kafka, etc.
5. 5. Experience with data pipeline and workflow management tools: Airflow, Prefect, etc.
6. 6. Experience with AWS cloud services: EC2, EMR, RDS, Redshift, Kinesis, etc.
7. 7. Experience with stream-processing systems: Storm, Spark-Streaming, etc.
8. 8. Must have experience leading projects and teams.
9. 9. Strong analytic skills related to working with unstructured datasets.
10. 10. Excellent communication and leadership skills.

Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot's policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

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