Responsible for designing, developing, and implementing AI-based solutions, including intelligent agents to automate processes and optimize operational workflows to drive business efficiency and improve client experience.
Responsibilities
Design and develop autonomous AI agents and multi-agent systems to automate complex business processes.
Create RAG (Retrieval-Augmented Generation) architectures integrating vector databases, business knowledge, and external APIs.
Implement client management agents connected via MCP to critical applications (CRM, media platforms, analytics, etc.).
Automate operational workflows in Google Marketing Platform (GMP), advertising platforms, and reporting tools.
Develop generative AI solutions to improve communications, insight generation, and repetitive tasks.
Requirements
University degree in Engineering
University degree in Data Science
University degree in Computer Science or similar
Master/specialization in Data Science, AI or Generative AI (valued)
Nice to have: Experience in media agency, digital marketing, or advertising environments
Consultant.e Data Engineer responsible for end‑to‑end data stack deployment, pipeline construction, data modeling, quality assurance and client support.
Responsibilities
Deploy a modern data stack end‑to‑end for clients
Build and maintain robust data pipelines
Model data for usability (SQL, data warehouse, datamarts)
Ensure data quality, reliability and monitoring
Support business teams in data understanding and usage
This role sits at the intersection of SRE, DevOps, and data, helping to improve reliability, observability, and operational maturity of data platforms, Kubernetes-based deployment systems, internal applications, and cloud environments.
Compensation
In the U.S., the standard base pay range for this role is $102,800.00 - $190,204.00 Annual.
Responsibilities
Monitor service health, respond to alerts, and participate in incident response for cloud, Kubernetes, application, and data platform environments.
Support the reliability of internal applications, APIs, workers, streaming consumers, event-driven services, and deployment workflows used by data engineering and business teams.
Build and maintain dashboards, alerting, runbooks, and operational documentation that improve detection and recovery speed.
Improve observability for Databricks environments, including job health, Spark streaming workloads, structured streaming metrics, cluster behavior, failures, latency, throughput, and cost signals.
The analyst will apply experience with Sales Cloud, Revenue Cloud, and Service Cloud to analyze data involving accounts, contacts, opportunities, and other critical business data entities and processes.
The Enterprise Data Management Lead drives the strategy, standards, and execution of enterprise data governance and lifecycle management practices across WSFS.
Compensation
Salary Range:
$111,440.00 - $183,082.25
Responsibilities
Develop and lead the strategy, implementation, and continuous improvement/maturation of Enterprise Data Management and Governance capabilities.
Maintain and evolve the data governance framework, including policies, standards, and operating procedures.
Partner with business lines to understand processes, data flows, and Critical Data Elements (CDEs) to ensure alignment with enterprise data standards.
Collaborate with Data Owners and Data Stewards to promote consistent data usage, quality, and lifecycle practices across the enterprise.
Recommend enhancements to enterprise data processes, governance workflows, and supporting tools to meet strategic objectives.
Requirements
Bachelor’s Degree in Computer Science, Information Systems, or related analytical discipline
Master’s Degree preferred or equivalent combination of education and/or work experience
Senior Data Scientist who designs, develops, and implements machine learning models, AI systems, and statistical modeling to generate actionable insights for business problems and product development.
Compensation
Pay Range: $108,960.00 - $163,440.00 Annual
Responsibilities
Designs, develops, programs and implements Machine Learning Models (Clustering, Classification or Regressions models).
Performs Statistical Modelling and Measurements by applying data engineering, feature engineering, statistical methods, ML modelling and AI techniques.
Develops and applies novel models on customer data to identify areas to increase profits and reduce losses.
Partners with experimentalists to translate models into testable hypotheses.
Senior Data Analyst role to drive strategic analysis focused on identification, evaluation, and execution of opportunities to drive growth and operational excellence for Blizzard games’ success in China.
Responsibilities
Construct the vision for product-wide analysis to find compelling and actionable business insights.
Address high level strategic questions through holistic planning including clarifying on the question, developing hypotheses, and building analytic structure.
Collaborate with cross-functional teams including product, marketing, developers and external partners to deliver impactful insights.
Scope and perform analyses, provide detailed analytical reports, develop KPIs, and produce self-service dashboards from a massive raw dataset to drive business decisions.
Effectively present findings and recommendations to senior leadership and other stakeholders.
Senior Data Ops Engineer, Data Activation & Products - ActivisionNewActivision Blizzard, Inc. · Los Angeles, US