Associate Data Engineer
Remote
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Sync Computing
Save up to 50% on compute with Sync's AI cluster management solution. Reduce compute spend, meet SLAs, and seamlessly adapt to changes.Company: Sync Computing
Role: Associate Data Engineer
Location: Atlanta
Position Type: Full-time
About Us:
Spun out of MIT, we’re a company founded on making cloud infrastructure easier to understand and control. Our team thrives on solving hard technical challenges and bringing solutions to the world. We are a forward-thinking company dedicated to delivering high-quality software solutions to our clients. Our team thrives on innovation, collaboration, and excellence. We are looking for a motivated and skilled Associate Customer Success Engineer to join our dynamic team.
Vision:
Data and AI workloads on the cloud are used in production in enterprises around the world, impacting revenue, costs, and product performance. However, the sheer complexity of these workloads makes it difficult to bridge low-level computing properties to high-level business goals. Our vision is to bridge this gap between computing and business and to make it easy for enterprises to stay efficient, agile, and informed. We call this vision – Declarative Computing.
Job Description:
We are seeking an Associate Customer Success Engineer with a passion for quality assurance and supporting our customers. Your primary responsibilities is to ensure Sync customers have a positive experience using our platform through rigorously testing and support.
The Impact You Will Have:
- Learn and become a Gradient expert! Customers and non technical team members will look to you for advice and expertise
- Develop, maintain, and execute automated and manual tests to ensure the platform is functioning properly, secure, and performs reliably under load
- Design, develop, and maintain configurable data pipelines using Apache Spark, SQL, and other relevant technologies
- Collaborate with cross-functional teams to understand data requirements and implement solutions that meet business needs
- Work with cloud-based platforms, particularly Databricks, to perform data processing and analytics on internal metrics
- Develop comprehensive reports and dynamic dashboards that provide critical insights and support for both field operations and our leadership team.
- Ensure data quality and integrity throughout the data pipeline lifecycle
- Stay current with industry trends and best practices in data engineering and analytics
What We Look For:
- Recent degree in Computer Science or a related field, or 2-4 years of related experience
- Desire to ensure customers are successful at achieving their desired outcome on the platform and that they have a positive user experience while doing so
- Proficiency in SQL, Apache Spark, and process orchestration tools like Airflow or Azure Data Factory
- Proficiency in Python and familiarity working with REST APIs and automation libraries
- Excellent problem-solving skills and attention to detail
- Strong communication and teamwork abilities
- Flexible startup mentality that is able to wear many hats due to changing business needs
- Collaborative “small team” mentality - highly collaborative, transparent, respectful, quick to respond (aka no team silos)
- A bias to action style of work, someone who “gets stuff done” and solves problems
Skills That Will Help You Hit the Ground Running:
- Experience with Databricks, Snowflake or similar data processing platforms is highly desired
- Cloud administration experience or relevant certifications (AWS, Azure, GCP)
Why Join Us:
- Opportunity to work with a talented and passionate team
- Exposure to cutting-edge technologies and methodologies
- Career growth and development opportunities
- Competitive salary
- 100% of your medical premium paid by us!
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Agile Airflow APIs AWS Azure Computer Science Databricks Data pipelines Data quality Engineering GCP Pipelines Python Snowflake Spark SQL Testing
Perks/benefits: Career development Competitive pay Flex hours Startup environment
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