Senior Machine Learning Engineer
Los Angeles, CA
Applications have closed
- Create and maintain optimal data pipeline architecture,
- 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 cloud-native and big data principles.
- Build the framework to enable analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Work with stakeholders including the Product, Data and Marketing teams to assist with data-related technical issues and support their data infrastructure needs.
- Keep our data separated and secure across national boundaries through multiple data centers and regions.
- Create data processes and tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
- Work with data and analytics experts to strive for greater functionality in our data systems.
- Advanced working knowledge and experience working with relational databases, query authoring as well as working familiarity with a variety of databases.
- Experience building and optimizing big data pipelines, architectures and data sets.
- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Experience with message queuing, stream processing, and highly scalable big data stores.
- Experience with end-to-end API design, implementation and sustainment.
- Strong analytic skills related to working with unstructured and structured datasets.
- Project management and organizational skills to self-scope and direct on a day-to-day basis.
- Experience supporting and working with cross-product/feature teams in a dynamic environment.
- The ideal candidate should also have experience using the following software/tools:
- Experience with big data tools: Hadoop, Spark, Kafka, Beam, etc.
- Experience with both relational SQL and NoSQL databases, including Postgres, MongoDB, etc.
- Experience with data pipeline and workflow management tools such as Apache Airflow, etc.
- Strong Experience with Code Management + DevOps: Github, Github Actions, Concourse CI, Terraform, Atlantis, etc.
- Experience with Google Cloud Platform (GCP) cloud services: BigQuery, DataFlow, PubSub, CloudSQL, Cloud Storage, Cloud Composer, VertexAI
- Knowledge of the FHIR standard and/or healthcare data
- Experience with object-oriented/object function scripting languages: Python, Go, Scala, Java, C++, etc.
Our Application Process: Applying to a role you love can be exhausting, and understanding the next steps can feel vague and uncertain. You have done the hard part of submitting your application; let's do ours by sharing potential next steps
- You should receive a confirmation email after submitting your application.
- A recruiter (not a computer) reviews all applications at League.
- If we see alignment with League's needs, a recruiter will reach out to learn more about your goals. The recruiter will also share the team-specific interview process depending on the roles you are exploring.
- The final step is an offer, which we hope you will accept!
- Prior to joining us, we conduct reference and background checks. Additional checks could be required for US Candidates, depending on the role you are exploring.
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Tags: Airflow APIs Architecture Big Data BigQuery Dataflow Data pipelines DevOps GCP GitHub Google Cloud Hadoop Java Kafka Machine Learning MongoDB NoSQL Pipelines PostgreSQL Privacy Python RDBMS Scala Spark SQL Terraform
Perks/benefits: Equity Health care Salary bonus
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