Data Pipeline Engineer (ML) Job at OSI Engineering, Seattle, WA

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  • OSI Engineering
  • Seattle, WA

Job Description

Our client is scaling production ML systems and needs a hands-on engineer to help build, maintain, and run essential ML data pipelines . You’ll own high-throughput data ingestion and transformation workflows (including image- and array-type modalities), enforce rigorous data quality standards, and partner with research and platform teams to keep models fed with reliable, versioned datasets.

  • Design, build, and operate reliable ML data pipelines for batch and/or streaming use cases across cloud environments.
  • Develop robust ETL/ELT processes (ingest, validate, cleanse, transform, and publish) with clear SLAs and monitoring.
  • Implement data quality gates (schema checks, null/outlier handling, drift and bias signals) and data versioning for reproducibility.
  • Optimize pipelines for distributed computing and large modalities (e.g., images, multi-dimensional arrays).
  • Automate repetitive workflows with CI/CD and infrastructure-as-code; document, test, and harden for production.
  • Collaborate with ML, Data Science, and Platform teams to align datasets, features, and model training needs.

Minimum Qualifications:

5+ years building and operating data pipelines in production.

  • Cloud: Hands-on with AWS , Azure , or GCP services for storage, compute, orchestration, and security.
  • Programming: Strong proficiency in Python and common data/ML libraries ( pandas , NumPy , etc.).
  • Distributed compute: Experience with at least one of Spark , Dask , or Ray .
  • Modalities: Experience handling image-type and array-type data at scale.
  • Automation: Proven ability to automate repetitive tasks (shell/Python scripting, CI/CD).
  • Data Quality: Implemented validation, cleansing, and transformation frameworks in production.
  • Data Versioning: Familiar with tools/practices such as DVC , LakeFS , or similar.
  • Languages: Fluent in English or Farsi .
  • Strongly PreferredSQL expertise (writing performant queries; optimizing on large datasets).
  • Data warehousing/lakehouse concepts and tools (e.g., Snowflake/BigQuery/Redshift; Delta/Lakehouse patterns).
  • Data virtualization/federation exposure (e.g., Presto/Trino) and semantic/metadata layers.
  • Orchestration (Airflow, Dagster, Prefect) and observability/monitoring for data pipelines.
  • MLOps practices (feature stores, experiment tracking, lineage, artifacts).
  • Containers & IaC (Docker; Terraform/CloudFormation) and CI/CD for data/ML workflows.
  • Testing for data/ETL (unit/integration tests, great_expectations or similar).
  • Soft Skills Executes independently and creatively ; comfortable owning outcomes in ambiguous environments.
  • Proactive communicator who collaborates cross-functionally with DS/ML/Platform stakeholders.

Location: Seattle, WA

Duration: 1+ year

Pay: $56/hr

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