Job Description

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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