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We are seeking an experienced Data Engineering / Data Infrastructure Team Lead to guide a high-performing team in designing, scaling, and managing the robust, cost-efficient infrastructure powering our solution.
In this role, you will balance technical excellence with people leadership. You will architect distributed data systems and cloud-native technologies to safeguard our clients' revenue while driving strategic initiatives that align with business objectives, operational efficiency, and team growth.
Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.
Your Arena:
Leadership & Mentorship: Lead, mentor, and scale a team of talented data and infrastructure engineers. Cultivate a culture of technical excellence, continuous learning, and psychological safety.
Data Infrastructure & FinOps: Own the architecture and evolution of our modern data stack, ensuring robust, scalable backend services while driving cloud cost management (FinOps) to maximize resource efficiency.
High-Performance Engineering: Oversee the design of distributed systems, real-time streaming, and batch pipelines capable of processing millions of daily transactions with minimal latency.
Operational Excellence: Champion Infrastructure-as-Code (IaC), rigorous security compliance, data governance, and deep observability (monitoring/alerting) across the R&D organization.
Strategic Collaboration: Act as the bridge between data engineering, product, data science, and core R&D teams to execute complex cross-functional initiatives.

Required Senior BI Analyst
About the role:
Data only creates value when people can see it, trust it, and act on it. As Senior BI Analyst, you own that last mile: the BI platform everyone opens to answer their questions, and the KPI definitions that make sure the numbers on every dashboard mean the same thing across every team.
This is the business-facing anchor of our analytics function. You'll build and govern the reporting layer, define the metrics that leadership runs the company on, and lead the Business Analyst guild - the network of domain analysts embedded across GTM, Finance, CS, and Product. You're equal parts platform owner, metrics authority, and enabler of the analysts around you.
If you love turning messy business questions into clear, trusted, self-serve answers - and raising the analytical bar for a whole company while you do it - this role is for you.
What youll own:
Own the BI platform end-to-end - Design and manage the dashboard architecture, set standards for report building and certification, manage access/governance, and keep the platform performant and trusted
Define and govern company-wide KPI metrics - Build a certified KPI catalog with documented logic and ownership; arbitrate metric definitions across teams so "win rate" means the same thing everywhere and is encoded once in the semantic layer
Drive self-serve analytics adoption - Empower stakeholders to answer their own questions by making the platform accessible, intuitive, and built on trusted definitions
Co-lead the Analytics guild - Convene and lead the company-wide community of practice connecting domain analysts; run enablement, tooling decisions, and reviews to keep everyone aligned on standards and the certified KPI catalog
Bridge analytics and engineering - Partner with the AI Data Engineer to ensure business definitions translate consistently into technical implementations across dashboards and AI agents
What you'll do day to day:
Design, build, and maintain governed dashboards and reports that stakeholders actually use.
Curate the certified KPI catalog - definitions, logic, ownership, and change control.
Partner with GTM, Finance, CS, and Product to translate business questions into clear, reusable metrics.
Drive self-serve BI adoption and reduce ad-hoc reporting requests.
Run the Business Analyst guild: standards, reviews, enablement, and tooling.
Work with data engineering to keep KPI definitions aligned with the semantic layer and underlying models.
Own the health of the BI platform: access, performance, certification, and cleanup.

Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.
Key responsibilities:
Lead a team of data scientists, data engineers and product analysts
Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation
Drive excellence, code quality, and best development practices
Provide technical direction and hands-on guidance for complex AI systems
Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences
Ensure AI solutions are safe, scalable, observable, and continuously improving
Lead initiatives around product data analysis and experimentation
Analyze user interactions with product features to improve accuracy, UX, and business impact
Partner with product teams to define metrics, dashboards, and experiments that guide product improvements
Design system architectures for AI-enabled applications at scale
Evaluate and select technologies for AI platforms and data pipelines
Guide the development of prompt engineering frameworks and centralized prompt management
Ensure robust monitoring, evaluation, and feedback loops for AI outputs
Translate product and business goals into technical roadmaps and execution plans
Drive alignment between AI capabilities and measurable product outcomes.

we are seeking a skilled and motivated Data Scientist. This individual will play a pivotal role in identifying key data points for collection, developing strategies to accumulate data and deriving actionable insights an anomaly based on a solid foundation of relevant know-how. Also, will also be responsible for creating, testing, and deploying scripts and methods for data collection and analysis to support decision-making. The Engineer will collaborate with cross-functional teams to identify critical data sources to determine the most effective data collection strategies, will develop automated and scalable data collection pipelines, will ensure data quality, integrity, and consistency across all sources and may use AI techniques to refine the results toward failures predictions.

We are seeking our Data Operations Team Leader!
This person will oversee a team of data operations specialists responsible for labeling data for machine learning and AI projects.
This role involves managing day-to-day operations, ensuring annotation quality and accuracy, developing guidelines, and collaborating with cross-functional teams to meet project deadlines and objectives.
What Youll Do:
Manage the end-to-end data workflow, from incoming data through annotation delivery to the ML teams, including overseeing data upload processes and ensuring data availability.
Manage database operations and ensure the integrity and availability of annotation data.
Become a Subject Matter Expert in our data and actively participate in hands-on annotation projects.
Create and maintain onboarding materials, annotation guidelines, and operational documentation to support knowledge sharing and consistent annotation practices.
Lead a team of Data Operations Specialists by managing task assignments, scheduling, and day-to-day execution. Provide the tools and processes needed for efficient work, ensure high annotation quality in collaboration with the ML teams, and continuously improve workflow efficiency
Own the annotation platform, driving bug fixes, feature development, and workflow improvements using Python and GenAI tools.
Develop and maintain operational dashboards and real-time performance metrics to monitor team efficiency, quality, and output.
Collaborate closely with data scientists, machine learning engineers, and product managers to understand annotation requirements and project goals. Coordinate cross-functional communication, monitor operational KPIs, and oversee daily operations to keep projects on track and ensure adherence to shifting schedules.

We are now hiring a talented, self-driven and passionate Senior Data Engineer to build and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting.
What Youll Do:
Design, develop, and maintain scalable data pipelines that integrate data from multiple sources, including APIs, databases, streaming platforms, edge computing devices, cloud services, and on-premise systems.
Design reliable and business-critical data processing workflows for batch and real-time use cases.
Develop data access services and APIs that enable efficient communication between edge devices, cloud infrastructure, and on-premise environments.
Design and optimize storage solutions for structured, semi-structured, and high-dimensional sensor data to support AI model training, inference, and analytics.
Strong understanding of distributed systems and scalable data processing architectures.
Build and maintain scalable streaming data pipelines for low-latency processing and event-driven architectures.
Analyze existing data architecture, storage models, and processing workflows, and continuously improve performance, scalability, reliability, and maintainability.
Optimize cloud infrastructure and data storage costs while maintaining high availability and low-latency access.
Collaborate closely with Software, AI, Algorithms, DevOps, and Product teams to translate business requirements into scalable technical solutions.
Design monitoring, observability, and operational processes for data platforms.

Were hiring a hands-on Senior Data Engineer who wants to build data products that move the needle in the physical world. Your work will help construction professionals make better, data-backed decisions every day.

Responsibilities:
Lead the design and development of scalable data pipelines.
Collaborate with Product, Data Science, and Analytics teams.
Build and optimize workflows using Databricks, Spark, Kafka, and AWS.
Implement real-time and batch processing architectures.
Develop and maintain ingestion pipelines from multiple sources.
Manage deployment and performance of data infrastructure.
Use Terraform to manage infrastructure-as-code.
Prepare data for analytics and product-facing use cases.

We are looking for an experienced Data Product Manager with a strong Data Science and Analytics background to own and lead data driven solutions.
In this role, you will define and drive the vision, strategy, and execution of data and analytics products that support our Marketing, Growth, and Acquisition teams. You will operate at the intersection of business, analytics, data science, and AI, translating marketing challenges and business questions into scalable data products, clear KPIs, and measurement frameworks that improve acquisition efficiency, ROI, and long-term revenue growth.
You will lead cross-departmental initiatives, partner closely with Marketing leaders, Data Scientists, Data Engineers, Analytics teams, and Product Managers, and ensure that advanced modeling, analytics, and AI capabilities are deeply embedded into day-to-day marketing decision-making.
Key Responsibilities
Marketing Data Product Ownership
Own the vision, roadmap, and delivery of Marketing Data Science and Analytics products
Align initiatives with business goals such as acquisition efficiency, ROI optimization, revenue growth, and funnel performance.
Act as the single point of ownership for marketing-related data, analytics, and data science products across teams.
Define data requirements, metrics, and success criteria for new initiatives, including identifying gaps in existing data.
Collaborate with Data Engineers and Analytics Engineers to design scalable data pipelines, data models, and semantic layers that support analytics, experimentation, and advanced modeling.
Prioritize initiatives based on business impact, scalability, data maturity, and clarity of success metrics.
Business Analytics, Data Science & AI-Driven Marketing Optimization
Define and own the business logic behind marketing KPIs, ensuring clear, consistent, and trusted metrics across teams.
Work closely with stakeholders to understand what needs to be measured, why and how.
Lead data science, analytics, and AI initiatives that optimize marketing performance
Own and evolve core marketing models including attribution, MMM, LTV prediction.
Define success metrics and measurement frameworks for models, experiments, and AI-driven decisions.
Drive experimentation frameworks (A/B tests, geo-experiments, uplift modeling) to continuously improve performance and decision quality.
Design and deploy AI-powered and agentic solutions to automate and scale marketing workflows such as budget allocation, campaign optimization, and creative testing.
Ensure solutions are production-ready, measurable, explainable, and actionable for business stakeholders.
Translate complex model outputs into clear, trusted insights for business users.

We are looking for a Senior Analytics Engineer to help design and build the engineering foundation that powers analytics across the organization.
Our goal is to create a modern data environment where analytics development is fast, reliable, scalable, and increasingly automated. This includes building strong data warehouse foundations, scalable modeling layers, and introducing AI-powered tools and automation that accelerate how data products are built and used.
In this role, you will be part of the data group, working closely with analytics engineers, analysts and business stakeholders while building the infrastructure, automation frameworks, and intelligent tooling that enable analytics to scale across the organization.
This is a unique opportunity to help build the next generation of the data organization.
Key Responsibilities:
Building tools and workflows that automate analytics development, dashboards, and data exploration
Design and build scalable data warehouse models and transformation layers
Build and optimize ETL pipelines and core analytics infrastructure (Bronze / Silver)
Improve performance, reliability, and scalability of the analytics platform
Develop automation and internal tools that accelerate analytics workflows
Enable self-serve data access across the company through semantic layers and reusable datasets.

we are seeking a senior leader to establish and grow its Databricks practice. This is a unique opportunity to build and lead a strategic Data & AI capability within a leading consulting firm, shaping its vision, offerings, team, and market presence.
The role combines business leadership, solution architecture, and client engagement, helping organizations leverage modern Data & AI platforms to drive transformation and innovation.
Job description
Lead and expand EY's Databricks practice, including strategy, capability development, team growth, and go-to-market initiatives.
Design and oversee enterprise-scale Data & AI solutions, including Lakehouse architectures, data modernization programs, and AI-driven platforms.
Build trusted relationships with senior stakeholders, lead business development efforts, and drive complex technology engagements.
Lead the successful delivery of large-scale Data & AI projects while mentoring and developing high-performing teams.

Serve as primary point of contact for data analysis initiatives, esp. for Finance and Commercial related data. Gather requirements from stakeholders across the company and translate them into actionable specifications for dashboards and reports. Design, develop, and maintain reliable and scalable dashboards, visualizations, and data solutions that support decision-making. Author high-quality design documents for reports and semantic layers. Establish and enforce robust data governance framework to ensure data integrity, data privacy, and compliance Partner closely with stakeholders to review data on a regular basis and surface actionable insights. Lead and mentor business data analysts across multiple units, ensuring consistent methodologies and best practices. Monitor and improve data quality, resolving discrepancies across reporting systems. Collaborate with IT, data Engineering, and business teams to integrate and optimize data sources for reporting solutions. Stay current with emerging data technologies and methodologies Promote a data -driven culture by driving analytics literacy and recommending process and tool improvements. Contribute to the data strategy and roadmap.

We are looking for a creative, independent Product Engineer to join our Networking business unit. For over two decades, Our Networking has continuously reinvented itself, delivering industry leading high speed interconnect products. Our solutions set the standard for performance, scalability, and innovation enabling true end to end high speed Ethernet and InfiniBand solutions.

As we continue to grow, we are seeking exceptional engineers to help drive the next wave of technological advancement. In this role, you will take a leading position in the Interconnect Cluster Performance domain. You will work closely with global technical teams to gain deep architectural insight, investigate customer complaints, and drive product excellence. If you are passionate about building high-quality products that make a real-world impact, we want to hear from you.

What Youll Be Doing:

Maintain high-quality product deliveries through in depth BIG data analysis and failure analysis.

Improve product performance by collaborating with Process, Mechanical, Hardware, and Thermal teams.

Lead high impact task forces and cross functional working groups.

Help Design and enhance product test plans to ensure high customer satisfaction.

Manage failure analysis for both field and lab issues.

Collaborate closely with Customer Support, Marketing, Engineering, Quality, and Reliability teams.

Rakuten Viber is one of the most popular and downloaded apps in the world. Working with us means influencing hundreds of millions of users and shaping the journey toward becoming a true super-app. Our mission is to make people’s lives easier by enabling meaningful connections — from special moments with friends and family, to building businesses and pursuing passions. We’re looking for a Senior Data Analyst who combines strong technical skills with a strategic mindset and curiosity for how data drives user experience and growth in social mobile apps. You’ll partner closely with product, engineering, and marketing teams to turn data into clear insights and measurable business impact. If you’re passionate about uncovering insights, leveraging data and AI for smarter, faster results, and driving decisions at scale; we’d love to hear from you!

Responsibilities:

* Identify opportunities for improvement through data analysis aligned with company strategy.
* Partner cross-functionally to translate data findings into actionable product and business decisions.
* Design, run, and analyze A/B tests to evaluate product changes and user experience optimizations.
* Build, automate, and maintain dashboards and reports to track platform health, user behavior, and key trends.
* Communicate findings clearly to stakeholders, influencing both tactical and strategic decisions.
* Leverage AI and automation tools to accelerate analytical workflows and uncover deeper insights.

Advantages:

* Background in statistics, causal inference, or experimentation frameworks.
* Experience mentoring analysts or leading analytical projects.
* A deep interest in social, communication, or engagement-driven apps.

Experience:
6+

As a Data Science Manager, you will lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions. You will own the end-to-end development lifecycle of AI projects, from exploratory research and experimentation through to scalable deployment and optimization. In this role, you will partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery. You will also drive innovation across a broad spectrum of ML domains, including supervised/unsupervised learning, anomaly detection, and generative AI, while providing hands-on guidance and championing best practices in MLOps, data governance, and model evaluation.
Key Responsibilities
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.