Lead Machine Learning Engineer

Responsibilities

• Design and implement scalable, secure data pipelines from internal systems of record to Google Cloud Platform services (Big Query, Big Table, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
• Leverage, extend and advise on capability roadmaps for reusable frameworks and tooling (ingestion, transformation, quality, orchestration) actively being developed by the larger organization.
• Enable self‑service data consumption and governance by standardizing patterns, templates, and sandbox capabilities rather than one‑off pipelines.
• Design data architectures for training, validation and monitoring of predictive machine learning as well as generative AI solutions.
• Define and implement standardized feature engineering and a common feature store with strong lineage, dictionary and high availability for models.
• Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies).
• Develop transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines).
• Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration.
• Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layer implementations with Big Query or similar tools.
• Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards.
• Partner with data scientists and domain solution teams to develop and deliver new model use cases onto GCP capabilities.
• Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples.

• 5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 5+ years of experience creating analytics or data science solutions in Public Cloud (GCP, AWS, Azure)
• 5+ years of hands on experience of Python and/or Go for building data pipelines, libraries, and automation tooling
• 5+ years with GCP or equivalent open source orchestration tools (Composer/Airflow/Dataflow/Beam) and CI/CD (Git, Liquibase, ) for data workloads
• 2+ years of hands‑on experience building and implementing predictive AI models using machine learning algorithms (e.g., regression, classification, forecasting).