5 data ops engineer jobs at 5 companies in Blue Point, NY
3w
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Principal Marketing Data Ops Engineer II
Bethesda or Getzville or New York City or Beachwood or Indianapolis or Tucson or Tampa or Chicago or Fredericksburg or Melville
HybridFull Time
7+ YOE7+ years building data pipelines in Python/Scala, 7+ years SQL with enterprise warehouses, 5+ years ETL/ELT and cloud infrastructure experience (Azure/AWS/GCP); BA/BS preferred.
Python, Scala, SQL, Snowflake, Databricks, Azure, AWS, GCP, Azure Data Factory, Microsoft Dynamics, Statsig, REST API
2w
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Data Operations Lead
Brooklyn, New York, United States
$150k-$250k/yr HybridFull Time
Technical program management experience owning ML/robotics data programs, understanding of ML training data and evaluation, cross-team coordination with research/engineering/ops, and ability to drive delivery.
SQL, Python
2mo
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Systems Manager - Data Operations, Enterprise Data & Analytics
New York, New York, United States
OnsiteFull Time
6+ YOEMaster's or Bachelor's in CS/Engineering/Math/Business with 6-8 years of relevant experience; strong data ops, governance, and cloud data platform skills; licensed driver; IT certifications preferred.
Google Cloud Platform, Azure Databricks, C3.ai, Databricks, Microsoft Purview, Dataplex
1mo
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Technical Lead Manager, MLOps
New York City or United States or Philadelphia or Boston
$199k-$241k/yr HybridFull Time
6+ YOEBachelor's degree plus 6+ years (or Master's plus 4+ years) in ML engineering; experience building ML platforms, training/serving infrastructure, feature stores, orchestration, monitoring, deployment pipelines; experience managing ML Platform/ML Ops teams; hands-on with Ray, Flink, Feast; cloud data tooling (AWS, Redshift, Databricks, Snowflake); s...
Ray, Flink, Feast, AWS, Redshift, Databricks, Snowflake, Python
2mo
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Digital Direct Individual Retirement Product, AVP
Sandy or Purchase
$85k-$140k/yr HybridFull Time
4+ YOE4+ years in digital retirement/product roles; experience with account opening, funding, rollovers, and retirement disclosures; UX/data-driven improvements; cross-functional collaboration across product, engineering, risk, ops, and marketing.