ABOUT THE ROLE AND YOUR TEAM:

TKWW is looking for a Senior Data Engineer to help grow all the data capabilities inside the company building a state-of-the-art data infrastructure. This person will be part of the DataOps team, collaborating closely with other data engineers, ML engineers and data scientists to develop tools and processes that will speed up their work. As a Data Engineer you will be working in a fast-paced environment and using cutting-edge technologies to develop a scalable data platform that will support years of company growth and build a best-in-class web product for couples.

RESPONSIBILITIES:

Be an essential part of the DataOps team.  Work with data science, data ops, and business stakeholders to deploy scalable processes to orchestrate tasks and move data around in a timely manner.  Assemble large, complex data sets that meet requirements to support data science and analytics projects.  Proactively identify, and implement internal process improvements including automating manual work, optimizing data delivery, re-designing infrastructure for greater scalability.   Support daily operations and production of our data platform, including monitoring, quality, and governance utilities, CI/CD pipelines, and all data integration touchpoints. 

SUCCESSFUL SENIOR DATA ENGINEER CANDIDATES HAVE:

Bachelor’s degree in Computer Science, Engineering or related field. Excellent communication skills in English (oral and written).  3+ years of engineering experience with 1+ years of experience building big data pipelines using Python, SQL, and Apache Spark.  Experience building high-performance APIs in Python using frameworks like FastAPI. Strong understanding of cloud architecture tools and services, such as AWS and cloud data warehouses. Working knowledge of open-source data orchestration tools such as Apache Airflow and experience applying them.  Experience working with a wide variety of databases (Vector, SQL, NoSQL). Willingness to learn new technologies and approaches to solve new problems. Snowflake experience is a plus. Experience on putting ML models into production will also be an advantage.