The Role:
We are seeking a highly motivated and experienced Data Engineer with deep expertise in MLOps discipline to join our Data Engineering team. In this role, you will be at the forefront of designing and developing scalable, robust data architectures and MLOps solutions utilizing the latest technologies from Google Cloud Platform (GCP) and AWS. As a key contributor, you will collaborate closely with cross-functional teams to understand and address their data and ML platform requirements.
Responsibilities:
Design, develop, and maintain scalable, high-performance data infrastructure to support the collection, storage, and processing of large datasets in real time and batch modes.
Build reliable, reusable services and APIs that allow teams to interact with the data platform for ingestion, transformation, and querying of data.
Build software tools, products and systems to monitor and manage the ML infrastructure and services efficiently.
Responsible for ensuring our ML systems are operating and running efficiently for model development, training, evaluation, and inference.
Collaborate with senior management, product management, and other engineers in the development of data products.
Develop tools to monitor, debug, and analyze data an ML pipelines.
Design and implement data schemas and models that can scale.
Mentor team members to build the company's overall expertise.
Work to make The RealReal an innovator in the space by bringing passion and new ideas to work every day.
Requirements:
At least 5 years of proven experience as a Data Engineer or MLOps Engineer in developing platform level capabilities for a data-driven midsize to large corporations.
Strong programming skills in languages such as Python, Java or Scala, with experience building large-scale, fault-tolerant systems.
Experience with cloud platforms (GCP, AWS, AZURE) with strong preference to GCP.
Experience with BigQuery or similar (Redshift, Snowflake, other MPP databases)
Hands-on experience with ML frameworks (TensorFlow, PyTorch) and ML deployment patterns.
Practical experience with containerization and orchestration tools (Docker, Kubernetes)
Experience building data pipelines & ETL
Experience with command line, version control software (git)
Excellent communication and collaboration skills.
Ability to work independently and quickly become productive after joining.
Preferred Requirements:
Knowledge of distributed data processing frameworks such as Apache Kafka, Flink, Spark, or similar.
Experience with DBT (Data Build Tool) and Looker.
Knowledge of common ML deployment patterns.
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