{"schemaVersion":"jobsearcher.job.v1","id":"29288288b17b4454a206e4f2","url":"https://jobsearcher.com/jobs/29288288b17b4454a206e4f2","canonicalUrl":"https://jobsearcher.com/jobs/29288288b17b4454a206e4f2","title":"Account Manager","description":"LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities, and support artificial intelligence initiatives., LatentView Analytics enables leading global brands to predict new revenue streams, anticipate product trends and popularity, improve customer retention rates, optimize investment decisions, and turn unstructured data into valuable business assets.\n\nDesignation : L4 (Account Manager)\n\nLocation : US (Remote)\n\nExperience : 9 - 15 years\n\nJob Role : Principal Data Engineer, AI Platforms\n\nResponsibilities :\n\nDetailed Skill Specifications\n\n1. Data Engineering Lead\n\nData Pipelines for AI Workloads: Deep expertise in building low-latency ingestion engines, unstructured data processing (audio, text, image, video ETL), and distributed processing using Apache Spark or Ray.\n\nVector & Feature Store Engineering: Hands-on experience operationalizing vector databases for retrieval-augmented generation (RAG) and low-latency feature stores for real-time model inference.\n\nContinuous Integration & MLOps Integration: Ability to orchestrate end-to-end retraining triggers based on data drift, upstream schema changes, and model degradation signals.\n\nTechnical Execution: Proven track record in code quality, distributed systems debugging, load testing, and database tuning.\n\n2. Data Engineering Strategist\n\nAI Readiness & Roadmapping: Ability to audit existing data assets, identify technical debt, and design multi-phase roadmaps that support generative and predictive AI initiatives.\n\nFinancial & Resource Modeling (Data FinOps): Experience forecasting cloud compute/storage costs associated with large-scale model training, vector indexing, and pipeline scaling.\n\nData Product Thinking: Treating data as an internal product by defining data contracts, domain-driven ownership (Data Mesh), and semantic layers for cross-functional consumption.\n\nRegulatory & Compliance Architecture: Formulating strict data retention, anonymization, and provenance standards to ensure AI training data remains compliant with emerging international AI regulations.\n\nRequired skills :\n\nPrincipal Data Engineer (AI Platforms)\n\nMust-Have Core Skills:\n\nAdvanced Programming & Querying:Fluency in Python, Scala, or Java, alongside advanced SQL (window functions, query plan optimization, CTEs).\n\nDistributed Data Processing:Real-world experience with Apache Spark, Ray, Apache Flink, and distributed computing patterns.\n\nStorage & Lakehouse Architecture: Hands-on design with Apache Iceberg, Delta Lake, Snowflake, or Databricks.\n\nOrchestration & Workflow Management:Apache Airflow, Prefect, Dagster, or dbt for transformation pipelines.\n\nAI Product Lifecycle & MLOps:\n\nVector & Feature Stores:Implementing and querying vector databases (Milvus, Pinecone, Qdrant) and feature stores (Feast, Hopsworks) for real-time inference and RAG pipelines.\n\nUnstructured Data Pipelines:Ingesting and preprocessing multi-modal data (text, documents, audio, images) for embedding generation and model training.\n\nData Observability & Drift Monitoring: Automated data quality testing (Great Expectations, Soda) and alerting on data/concept drift.\n\nCloud, Systems & DevOps:\n\nContainerization & orchestration (Docker, Kubernetes/EKS/GKE).\n\nCloud platform data services (AWS, GCP, or Azure) with Infrastructure as Code (Terraform).\n\nCI/CD automation for data and model pipelines.\n\nPrincipal Data Strategist (AI & Governance)\n\nMust-Have Core Skills:\n\nEnterprise Data Roadmapping: Designing 3-to-5-year data strategies that directly align with commercial AI product roadmaps.\n\nModern Data Stack Evaluation: Structuring \"build vs. buy\" decision frameworks, vendor RFP evaluations, and total cost of ownership (TCO) analyses.\n\nData Product Architecture: Applying Data Mesh principles, establishing data contracts, and defining semantic layers across domain teams.\n\nAI Readiness & Financial Modeling:\n\nData FinOps: Forecasting and optimizing compute, vector index storage, and inference API costs across the AI lifecycle.\n\nTraining Data Maturity & Lineage: Assessing data quality, provenance, and readiness for proprietary model fine-tuning and retrieval systems.\n\nGovernance, Risk & Compliance:\n\nResponsible AI & Regulatory Compliance: Designing policy controls aligned with the EU AI Act, GDPR, HIPAA, and IP licensing for training datasets.\n\nData Access & Security Governance: Defining RBAC/ABAC models, PII masking, data lineage tracking (OpenLineage), and audit readiness.\n\nExecutive Leadership:\n\nTranslating deep technical data constraints into business value for C-suite stakeholders (CDO, CTO, VP of Product)\n\nAt LatentView Analytics, we value a diverse, inclusive workforce and provide equal employment opportunities for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual's race, colour, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.\nJob Snapshot\nUpdated Date\n27-08-2026\nJob ID\nJ_5738\nLocation\nBellevue, Washington, United States\nExperience\n10 - 15 Years\nEmployee Type\nPermanent","company":"Latentviewanalytics","rawCompany":"latentviewanalytics","city":"Seattle","state":"WA","isRemote":false,"isActive":false,"createdAt":"2026-08-29T10:44:50.522Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Account Manager","description":"LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities, and support artificial intelligence initiatives., LatentView Analytics enables leading global brands to predict new revenue streams, anticipate product trends and popularity, improve customer retention rates, optimize investment decisions, and turn unstructured data into valuable business assets.\n\nDesignation : L4 (Account Manager)\n\nLocation : US (Remote)\n\nExperience : 9 - 15 years\n\nJob Role : Principal Data Engineer, AI Platforms\n\nResponsibilities :\n\nDetailed Skill Specifications\n\n1. Data Engineering Lead\n\nData Pipelines for AI Workloads: Deep expertise in building low-latency ingestion engines, unstructured data processing (audio, text, image, video ETL), and distributed processing using Apache Spark or Ray.\n\nVector & Feature Store Engineering: Hands-on experience operationalizing vector databases for retrieval-augmented generation (RAG) and low-latency feature stores for real-time model inference.\n\nContinuous Integration & MLOps Integration: Ability to orchestrate end-to-end retraining triggers based on data drift, upstream schema changes, and model degradation signals.\n\nTechnical Execution: Proven track record in code quality, distributed systems debugging, load testing, and database tuning.\n\n2. Data Engineering Strategist\n\nAI Readiness & Roadmapping: Ability to audit existing data assets, identify technical debt, and design multi-phase roadmaps that support generative and predictive AI initiatives.\n\nFinancial & Resource Modeling (Data FinOps): Experience forecasting cloud compute/storage costs associated with large-scale model training, vector indexing, and pipeline scaling.\n\nData Product Thinking: Treating data as an internal product by defining data contracts, domain-driven ownership (Data Mesh), and semantic layers for cross-functional consumption.\n\nRegulatory & Compliance Architecture: Formulating strict data retention, anonymization, and provenance standards to ensure AI training data remains compliant with emerging international AI regulations.\n\nRequired skills :\n\nPrincipal Data Engineer (AI Platforms)\n\nMust-Have Core Skills:\n\nAdvanced Programming & Querying:Fluency in Python, Scala, or Java, alongside advanced SQL (window functions, query plan optimization, CTEs).\n\nDistributed Data Processing:Real-world experience with Apache Spark, Ray, Apache Flink, and distributed computing patterns.\n\nStorage & Lakehouse Architecture: Hands-on design with Apache Iceberg, Delta Lake, Snowflake, or Databricks.\n\nOrchestration & Workflow Management:Apache Airflow, Prefect, Dagster, or dbt for transformation pipelines.\n\nAI Product Lifecycle & MLOps:\n\nVector & Feature Stores:Implementing and querying vector databases (Milvus, Pinecone, Qdrant) and feature stores (Feast, Hopsworks) for real-time inference and RAG pipelines.\n\nUnstructured Data Pipelines:Ingesting and preprocessing multi-modal data (text, documents, audio, images) for embedding generation and model training.\n\nData Observability & Drift Monitoring: Automated data quality testing (Great Expectations, Soda) and alerting on data/concept drift.\n\nCloud, Systems & DevOps:\n\nContainerization & orchestration (Docker, Kubernetes/EKS/GKE).\n\nCloud platform data services (AWS, GCP, or Azure) with Infrastructure as Code (Terraform).\n\nCI/CD automation for data and model pipelines.\n\nPrincipal Data Strategist (AI & Governance)\n\nMust-Have Core Skills:\n\nEnterprise Data Roadmapping: Designing 3-to-5-year data strategies that directly align with commercial AI product roadmaps.\n\nModern Data Stack Evaluation: Structuring \"build vs. buy\" decision frameworks, vendor RFP evaluations, and total cost of ownership (TCO) analyses.\n\nData Product Architecture: Applying Data Mesh principles, establishing data contracts, and defining semantic layers across domain teams.\n\nAI Readiness & Financial Modeling:\n\nData FinOps: Forecasting and optimizing compute, vector index storage, and inference API costs across the AI lifecycle.\n\nTraining Data Maturity & Lineage: Assessing data quality, provenance, and readiness for proprietary model fine-tuning and retrieval systems.\n\nGovernance, Risk & Compliance:\n\nResponsible AI & Regulatory Compliance: Designing policy controls aligned with the EU AI Act, GDPR, HIPAA, and IP licensing for training datasets.\n\nData Access & Security Governance: Defining RBAC/ABAC models, PII masking, data lineage tracking (OpenLineage), and audit readiness.\n\nExecutive Leadership:\n\nTranslating deep technical data constraints into business value for C-suite stakeholders (CDO, CTO, VP of Product)\n\nAt LatentView Analytics, we value a diverse, inclusive workforce and provide equal employment opportunities for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual's race, colour, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.\nJob Snapshot\nUpdated Date\n27-08-2026\nJob ID\nJ_5738\nLocation\nBellevue, Washington, United States\nExperience\n10 - 15 Years\nEmployee Type\nPermanent","datePosted":"2026-08-29T10:44:50.522Z","dateModified":"2026-08-29T10:44:50.522Z","hiringOrganization":{"@type":"Organization","name":"Latentviewanalytics","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Seattle","addressRegion":"WA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"29288288b17b4454a206e4f2"},"url":"https://jobsearcher.com/jobs/29288288b17b4454a206e4f2"}}