Account Manager

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. Designation : L4 (Account Manager) Location : US (Remote) Experience : 9 - 15 years Job Role : Principal Data Engineer, AI Platforms Responsibilities : Detailed Skill Specifications 1. Data Engineering Lead Data 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. Vector & Feature Store Engineering: Hands-on experience operationalizing vector databases for retrieval-augmented generation (RAG) and low-latency feature stores for real-time model inference. Continuous Integration & MLOps Integration: Ability to orchestrate end-to-end retraining triggers based on data drift, upstream schema changes, and model degradation signals. Technical Execution: Proven track record in code quality, distributed systems debugging, load testing, and database tuning. 2. Data Engineering Strategist AI Readiness & Roadmapping: Ability to audit existing data assets, identify technical debt, and design multi-phase roadmaps that support generative and predictive AI initiatives. Financial & Resource Modeling (Data FinOps): Experience forecasting cloud compute/storage costs associated with large-scale model training, vector indexing, and pipeline scaling. Data Product Thinking: Treating data as an internal product by defining data contracts, domain-driven ownership (Data Mesh), and semantic layers for cross-functional consumption. Regulatory & Compliance Architecture: Formulating strict data retention, anonymization, and provenance standards to ensure AI training data remains compliant with emerging international AI regulations. Required skills : Principal Data Engineer (AI Platforms) Must-Have Core Skills: Advanced Programming & Querying:Fluency in Python, Scala, or Java, alongside advanced SQL (window functions, query plan optimization, CTEs). Distributed Data Processing:Real-world experience with Apache Spark, Ray, Apache Flink, and distributed computing patterns. Storage & Lakehouse Architecture: Hands-on design with Apache Iceberg, Delta Lake, Snowflake, or Databricks. Orchestration & Workflow Management:Apache Airflow, Prefect, Dagster, or dbt for transformation pipelines. AI Product Lifecycle & MLOps: Vector & Feature Stores:Implementing and querying vector databases (Milvus, Pinecone, Qdrant) and feature stores (Feast, Hopsworks) for real-time inference and RAG pipelines. Unstructured Data Pipelines:Ingesting and preprocessing multi-modal data (text, documents, audio, images) for embedding generation and model training. Data Observability & Drift Monitoring: Automated data quality testing (Great Expectations, Soda) and alerting on data/concept drift. Cloud, Systems & DevOps: Containerization & orchestration (Docker, Kubernetes/EKS/GKE). Cloud platform data services (AWS, GCP, or Azure) with Infrastructure as Code (Terraform). CI/CD automation for data and model pipelines. Principal Data Strategist (AI & Governance) Must-Have Core Skills: Enterprise Data Roadmapping: Designing 3-to-5-year data strategies that directly align with commercial AI product roadmaps. Modern Data Stack Evaluation: Structuring "build vs. buy" decision frameworks, vendor RFP evaluations, and total cost of ownership (TCO) analyses. Data Product Architecture: Applying Data Mesh principles, establishing data contracts, and defining semantic layers across domain teams. AI Readiness & Financial Modeling: Data FinOps: Forecasting and optimizing compute, vector index storage, and inference API costs across the AI lifecycle. Training Data Maturity & Lineage: Assessing data quality, provenance, and readiness for proprietary model fine-tuning and retrieval systems. Governance, Risk & Compliance: Responsible AI & Regulatory Compliance: Designing policy controls aligned with the EU AI Act, GDPR, HIPAA, and IP licensing for training datasets. Data Access & Security Governance: Defining RBAC/ABAC models, PII masking, data lineage tracking (OpenLineage), and audit readiness. Executive Leadership: Translating deep technical data constraints into business value for C-suite stakeholders (CDO, CTO, VP of Product) At 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. Job Snapshot Updated Date 27-08-2026 Job ID J_5738 Location Bellevue, Washington, United States Experience 10 - 15 Years Employee Type Permanent