Senior Data Engineer

TripleLift•New York, NY

18d•$130,000 - $170,000

About The Position

TripleLift is seeking a Senior Data Engineer to join a small, influential Data Engineering team. This hire will be responsible for expanding and optimizing our data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. The Senior Data Engineer will support our software engineers, product managers, business intelligence analysts and data scientists on data initiatives, and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our company’s data architecture to support our next generation of products and data initiatives.

Requirements

Bachelor’s degree in Computer Science, a related technical field, or equivalent professional experience.
Minimum of 5 years of professional software engineering experience, with a strong focus on data and ML processes running in production at scale.
Experience with optimizing big data and ML pipelines like Spark and Kafka at scale
Fundamental knowledge of ML and AI operations and processes.
Understanding of a variety of database technologies such as OpenSearch, Druid, Clickhouse, InfluxDB, etc
Proven track record of cross-functional data modeling
Strong command of SQL and expertise in one or familiarity with multiple of the following languages: Python, Scala, Java

Nice To Haves

Experience in MLFlow, Feast, Ray, and other AI training technologies a plus
Experience in containerization, K8s and ArgoCD is a plus

Responsibilities

Create and maintain optimal data pipeline architecture,
Explore and assemble large, complex data sets that meet functional / non-functional business requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Spark, EMR, Kafka and other big data technologies
Work with stakeholders across different teams, including product managers, engineers and analysts to assist with data-related technical issues and support their data infrastructure needs.