About Payoneer

Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world’s underserved businesses to a rising global economy. We’re a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 markets.

By taking the complexity out of the financial workflows–including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence–we give businesses the tools they need to work efficiently worldwide and grow with confidence.

About the Skuad Acquisition
Back in August 2024, Payoneer acquired Skuad. Skuad's expertise helps businesses in over 160 countries navigate complex challenges such as international payroll, remote onboarding, and regulatory compliance. This acquisition reinforces Payoneer's mission and solidifies Skuad's role as a trusted partner for global workforce management (WFM).

This role will be part of our WFM business unit.

Role summary:

On-site
Full-time

We are looking for an experienced Global Procurement Manager – Employee Benefits to lead the setup and implementation of employee benefits across the globe. This role will focus on enabling new benefits, ensuring compliance, managing governance frameworks, and acting as an escalation point for complex benefits matters.

Key benefit areas include insurance, company-issued devices, co-working spaces, meal and other vouchers, car lease, background verification, and medical checks/health & safety programs.

This position will play a critical role in building scalable frameworks for global benefits delivery, ensuring consistent execution and alignment across countries

What you’ll do:

Global Benefits Enablement & Governance-

• Lead the implementation and setup of new benefits programs across multiple countries, covering health and life insurance, devices, co-working spaces, vouchers, and related offerings.
• Research and benchmark local market practices to design compliant, competitive, and cost-efficient benefits packages.
• Partner with HR Operations, Legal, Finance, and Payroll teams to operationalise benefits programs and ensure smooth rollouts.
• Develop and maintain standardised processes, documentation, and governance frameworks to support scalable and efficient global benefits management.
• Be responsible for end-to-end vendor/partner management, including onboarding, negotiation, contracting, invoicing, payments, and vendor reviews.

Escalation Management & Cross-Functional Collaboration-

• Act as the escalation point for complex or critical benefits-related queries, exceptions, or compliance issues.
• Collaborate with HR Operations on enrolment, de-enrolment, and vendor coordination, providing subject-matter expertise and guidance.
• Oversee vendor performance, vendor contracts, renewals, and benefit structures to maintain policy consistency and legal compliance.
• Provide structured recommendations and insights to leadership on program design, effectiveness, and improvement opportunities.

Reporting & Insights- 

• Monitor benefit utilisation, spend trends, and vendor performance to identify risks or optimisation opportunities.
• Prepare reports and insights for leadership on country-wise benefits of performance, compliance status, and upcoming renewals.

Who you are:

• 5+ years of experience in procurement, vendor management, partnerships, global benefits, or related fields.
• Strong understanding of international benefits, insurance programs, and compliance frameworks is a plus.
• Excellent analytical, problem-solving and communication skills.
• Proficiency in Microsoft Office Suite and AI tools like Copilot, ChatGPT, Claude, etc. (good to have)
• Ability to collaborate cross-functionally and operate independently in a global, fast-paced environment.
• High attention to detail with strong project management and organisational abilities.
• Agility, Speed of execution, and likeness to thrive in high growth environment as attributes for a candidate. We are like that :)

Role Summary

We are hiring Senior Engineers who combine solid software engineering fundamentals with proven proficiency in AI-assisted development. This role is designed for engineers who have actively used AI coding tools (such as Cursor, Claude Code, Codex CLI only) for at least one year and can leverage these capabilities to ship higher-quality code faster. You will be embedded in product engineering squads, leading feature development, mentoring junior engineers, and championing AI-first development practices across the team.

What Makes This Role Different

This is not an AI research role - it is a core engineering role for engineers who use AI as a force multiplier. We expect you to:

• Use AI coding assistants daily to accelerate development without sacrificing code quality.
• Critically evaluate AI-generated code for correctness, security vulnerabilities, and maintainability.
• Share best practices for AI-assisted workflows with the broader engineering team.
• Help define team standards for responsible and effective AI tool adoption.

Key Responsibilities

1  Feature Engineering & Delivery

• Design, build, and ship production-quality features across the full stack or within your domain (backend, frontend, platform, or data).
• Leverage AI coding assistants to write, refactor, document, and test end-to-end code efficiently.
• Translate product requirements and technical specifications into clean, maintainable implementations.
• Lead feature squads for medium-complexity initiatives from design through deployment.
• Participate in and drive sprint planning, backlog grooming, and technical estimation sessions.

2  AI-Augmented Development Practices

• Champion the use of AI pair-programming tools (Copilot, Cursor, Claude, etc.) across the engineering org.
• Develop and document internal guidelines for effective AI prompt engineering within coding workflows.
• Conduct training sessions or knowledge-share presentations on maximising productivity with AI tools.
• Critically audit AI-generated code for logic errors, security issues, and performance anti-patterns before merging.
• Stay updated on the latest AI development tooling and evaluate new tools for team adoption.

3  Code Quality & Engineering Excellence

• Write testable, modular, and well-documented code following the team's style guides and design principles.
• Drive test-driven development (TDD) and maintain code coverage targets.
• Perform thorough code reviews with constructive feedback; uphold engineering standards.
• Identify and address technical debt proactively; contribute to architecture improvements.
• Participate in RCA (root cause analysis) for production incidents; implement lasting fixes.

4  Architecture & Technical Design

• Contribute to system design discussions, proposing solutions with appropriate trade-off analysis.
• Create and maintain technical design documents (TDDs), ADRs (Architecture Decision Records), and API contracts.
• Evaluate and adopt relevant third-party libraries, APIs, and SaaS tools with a security-first mindset.
• Collaborate with platform / DevOps teams on observability, CI/CD pipelines, and infrastructure-as-code.

5  Mentoring & Team Growth

• Mentor 1–2 mid-level engineers; conduct regular 1:1 technical coaching sessions.
• Onboard new team members by sharing codebase context, tooling setup, and team practices.
• Contribute to a culture of psychological safety, learning from failures, and continuous improvement.

6  Cross-functional Collaboration

• Work closely with Product Managers, Designers, and Operations to deliver delightful, high-quality user experiences.
• Coordinate with data and AI/ML teams to integrate model endpoints, APIs, and AI features into product surfaces.
• Participate in customer-facing escalations where deep technical expertise is required.

Required Qualifications

1  Education

• Bachelor's degree in Computer Science, Software Engineering, or a related field.
• Candidates without a degree but with a strong portfolio and demonstrable project history will be considered.

2  Experience

• 3 – 5 years of professional software engineering experience in a product-focused environment.
• Minimum 1 year of active, hands-on experience using AI coding tools in professional work (not just personal projects).
• Track record of shipping production features end-to-end with measurable business impact.

3  Technical Skills — Core Engineering

• Languages: Proficiency in at least one of — Python, TypeScript/JavaScript, Java, Go, or Kotlin.
• Web / API: REST, GraphQL, gRPC; familiarity with microservices and event-driven architectures.
• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, DynamoDB).
• Cloud: AWS, GCP, or Azure — compute, storage, serverless, and managed services.
• DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins, CircleCI); IaC basics (Terraform/Pulumi).
• Testing: Unit, integration, and e2e testing frameworks; TDD/BDD familiarity.
• Version Control: Git, branching strategies (GitFlow, trunk-based), PR workflows.

4  AI Tooling Skills (Mandatory)

• Demonstrated proficiency with at least one AI coding assistant (Cursor IDE, Claude Code, Codex CLI).
• Ability to craft effective prompts for code generation, refactoring, debugging, and documentation tasks.
• Experience reviewing and validating AI-generated code for correctness and security.
• Understanding of limitations of LLM-based coding tools (hallucinations, context window, license risks).

5  Nice to Have

• Contributions to open-source projects.
• Experience building or integrating AI/ML features (e.g., calling LLM APIs, embedding models, RAG pipelines).
• Familiarity with agentic coding frameworks (LangChain, LlamaIndex, AutoGen, CrewAI).
• Experience in a startup or scale-up environment with rapid iteration cycles.
• Knowledge of web security (OWASP Top 10, secure coding practices).

Behavioural Competencies

• AI-First Mindset: Naturally reaches for AI tools to accelerate tasks while maintaining engineering rigour.
• Ownership: Takes end-to-end accountability — from design to deployment to monitoring.
• Pragmatism: Balances perfect with shipped; knows when to iterate vs. over-engineer.
• Mentorship: Invests in the growth of peers and junior colleagues.
• Communication: Writes clearly (code comments, docs, Slack) and articulates trade-offs confidently.
• Curiosity: Continuously explores new tools, languages, and architectural patterns.
• Collaboration: Works well in cross-functional squads; builds trust quickly.

Role Summary

We are seeking a Lead Engineer, AI Systems to serve as the technical anchor across our AI-augmented engineering organisation. This role bridges our Senior AI Coding Engineers, the AI/ML Developer (SLM Specialist), and the Agentic Systems intern cohort. You will set technical direction, own cross-cutting architectural decisions, and champion the responsible, high-impact adoption of AI-assisted development practices across the entire engineering function.

You will remain hands-on with code while simultaneously shaping how the wider team builds, evaluates, and ships AI-powered products.

Key Responsibilities

1  Technical Vision & Architecture

• Define and own the end-to-end technical architecture for AI Systems — spanning product feature surfaces, model inference APIs, and agentic toolchains.
• Drive Architecture Decision Records (ADRs), system design reviews, and RFC processes across squads.
• Establish standards for integrating SLM/LLM model endpoints into product surfaces built by the Senior Engineering team.
• Evaluate emerging AI infrastructure patterns (RAG, agentic orchestration, vector stores, model serving) and guide adoption decisions.
• Own the technical roadmap for AI tooling, developer productivity, and model integration layers.

2  Cross-Squad Technical Leadership

• Act as the primary technical liaison between the AI/ML Developer (model side) and Senior Engineers (product side), ensuring smooth API contracts, evaluation loops, and deployment handoffs.
• Provide technical direction to Agentic Systems interns, reviewing designs, code, and agentic pipeline implementations.
• Unblock senior engineers on hard architectural or integration challenges that span squad boundaries.
• Run cross-team design reviews, architecture syncs, and engineering guild sessions.

3  AI-Augmented Engineering Excellence

• Define and maintain the organisation’s standards for AI-assisted development — covering context engineering, AI code review protocols, context management, and tool evaluation criteria.
• Maintain and evolve the internal AI tooling playbook (Cursor IDE, Claude Code, Codex CLI, and emerging tools).
• Evaluate new AI coding tools, agentic frameworks (LangChain, LlamaIndex, CrewAI, AutoGen), and developer-productivity platforms; produce adoption recommendations with measured trade-offs.
• Conduct structured audits of AI-generated code across squads for correctness, security, and maintainability.

4  Hands-On Engineering

• Remain an active contributor: own critical-path features, prototype architectural spikes, and build shared infrastructure components used across squads.
• Personally drive resolution of the most complex production incidents and root-cause analyses.
• Review and merge high-impact PRs; maintain the highest code review quality bar on the team.
• Own observability and reliability for AI inference and MLOps integration layers in production.

5  Mentoring & Talent Development

• Mentor Senior Engineers, the AI/ML Developer, and interns through technical coaching, design feedback, and stretch assignments.
• Lead hiring panels and technical interviews; help define and uphold the engineering hiring bar.
• Contribute to onboarding frameworks that embed AI-first practices from day one.
• Model a culture of rigorous experimentation, psychological safety, and continuous improvement.

6  Stakeholder & Product Collaboration

• Partner with the Director of Engineering and Product leadership to translate product strategy into a phased technical roadmap.
• Present architectural proposals and trade-off analyses to engineering leadership and executive stakeholders as required.
• Coordinate with DevOps/Platform teams on GPU/TPU compute provisioning, CI/CD for model pipelines, and cloud cost optimisation.

Required Qualifications

1  Education

• Bachelor’s or Master’s degree in Computer Science, or a related field.
• Candidates without a degree but with a compelling portfolio demonstrating scope and impact at staff/lead level will be considered.

2  Experience

• 6 – 8 years of professional software engineering experience in product-focused environments.
• Minimum 2 years in a formal or de-facto technical lead / staff engineer capacity across multiple squads or systems.
• Minimum 8-12 months of active, hands-on experience with AI coding tools in a professional engineering setting.
• Proven track record of shipping production systems with measurable business impact at scale.
• Demonstrated experience collaborating closely with ML/AI model teams on integration, deployment, and evaluation.

3  Technical Skills — Core Engineering

• Languages: Expert proficiency in at least two of — Python, TypeScript/JavaScript, Go.
• Architecture: Microservices, event-driven systems, API design (REST, GraphQL, gRPC), distributed systems fundamentals.
• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis); vector databases (FAISS, Pinecone, Weaviate).
• Cloud: AWS, GCP, or Azure — compute, storage, serverless, managed ML services (SageMaker, Vertex AI).
• DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins); IaC (Terraform/Pulumi); observability stacks.
• Testing: TDD/BDD; unit, integration, and e2e frameworks; model evaluation pipelines.

4  AI / ML Integration Skills (Mandatory)

• Demonstrated proficiency with AI coding assistants (Cursor IDE, Claude Code, Codex CLI) in daily professional workflows.
• Experience designing and consuming LLM/SLM inference APIs; understanding of model serving, latency, and cost trade-offs.
• Hands-on familiarity with RAG architectures, vector stores, and retrieval pipelines.
• Ability to define and enforce AI code quality standards across an engineering team.
• Understanding of LLM limitations: hallucinations, context window constraints, prompt injection risks, and licensing considerations.

5  Technical Skills — Nice to Have

• Experience with SLM/LLM training or fine-tuning pipelines (SFT, RLHF, LoRA/QLoRA).
• Familiarity with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) and multi-step reasoning pipelines.
• Contributions to open-source ML or developer tooling projects.
• Knowledge of OWASP Top 10, secure coding, and AI-specific security risks (prompt injection, model exfiltration).
• Prior experience in a high-growth startup or scale-up environment with rapid iteration cycles.

Behavioural Competencies

• AI-First Mindset: Defaults to AI tools to accelerate work while maintaining rigorous quality standards; actively pushes the team’s AI capability forward.
• Systems Thinking: Sees the full picture — how model outputs, product surfaces, and infrastructure interdepend; anticipates second-order effects of architectural choices.
• Ownership: Takes end-to-end responsibility from design through production; doesn’t hand off problems, solves them.
• Influence Without Authority: Earns trust through technical credibility; drives alignment through persuasion, data, and well-reasoned proposals.
• Communication: Writes clear design docs, ADRs, and post-mortems; articulates complex trade-offs for engineering peers and non-technical stakeholders.
• Mentorship: Actively invests in the growth of engineers at all levels; gives direct, constructive feedback.
• Pragmatism: Balances the ideal with the shipped; knows when to iterate vs. refactor vs. rewrite.
• Curiosity: Continuously explores emerging research, tools, and patterns — and brings those learnings back to the team.

Role Summary

We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions — from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned.

Key Responsibilities

1  Model Design & Training

• Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX.
• Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning.
• Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters.
• Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results.
• Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora.

2  Model Evaluation & Quality

• Define and implement evaluation frameworks including perplexity, BLEU, ROUGE, BERTScore, and task-specific benchmarks.
• Conduct red-teaming, bias analysis, and safety evaluations to ensure responsible AI deployment.
• Benchmark models against established baselines (e.g., GPT-2, Phi, Mistral) and track performance over iterations.
• Collaborate with QA teams to build regression suites for model versioning and continuous evaluation.

3  MLOps & Deployment

• Containerise and deploy models using Docker, Kubernetes, and cloud-native ML platforms (AWS SageMaker / GCP Vertex AI / Azure ML).
• Build and maintain model registries, experiment tracking (MLflow, Comet), and reproducible training pipelines.
• Optimise inference performance through quantisation (INT8, INT4), pruning, distillation, and ONNX/TensorRT conversion.
• Monitor model drift, data drift, and performance degradation in production; implement automated retraining triggers.

4  Research & Innovation

• Stay current with state-of-the-art NLP/LLM research; prototype and validate new techniques from published literature.
• Contribute to internal knowledge sharing, technical documentation, and model cards.
• Explore parameter-efficient fine-tuning (PEFT) methods such as LoRA, QLoRA, and Adapter layers.
• Investigate and apply mixture-of-experts (MoE), retrieval-augmented generation (RAG), and agentic workflows as needed.

5  Collaboration & Stakeholder Management

• Work with product managers and domain experts to translate business requirements into model objectives and KPIs.
• Communicate model capabilities, limitations, and trade-offs clearly to both technical and non-technical stakeholders.
• Participate in architecture reviews, sprint planning, and cross-functional design discussions.

Required Qualifications

1  Education

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field.
• Equivalent professional experience with a strong portfolio of delivered AI/ML projects will be considered.

2  Experience

• 4 – 6 years of professional experience in machine learning or NLP engineering.
• Minimum 2 years of direct, hands-on experience in training or fine-tuning language models (LLMs or SLMs).
• Demonstrable experience taking a model from dataset preparation through to production deployment.

3  Technical Skills — Core

• Programming: Python (proficient); familiarity with C++/CUDA a plus.
• Deep Learning Frameworks: PyTorch (primary), TensorFlow or JAX.
• NLP Libraries: Hugging Face Transformers, Datasets, PEFT, TRL, Accelerate.
• Training Infrastructure: Multi-GPU training, FSDP, DeepSpeed ZeRO stages 1–3.
• Data Engineering: Pandas, NumPy, Apache Spark or Dask for large-scale data prep.
• Vector DBs & Retrieval: FAISS, Pinecone, Weaviate, or equivalent.
• Cloud Platforms: AWS, GCP, or Azure — ML-specific services (SageMaker, Vertex AI, etc.).
• Version Control & CI/CD: Git, GitHub/GitLab, MLflow or W&B for experiment tracking.

4  Technical Skills — Nice to Have

• Experience with multimodal models or vision-language models (VLMs).
• Knowledge of model safety, alignment techniques, and responsible AI frameworks.
• Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.
• Contributions to open-source ML projects or publications at NeurIPS, ICML, EMNLP, ACL.

Behavioural Competencies

• Intellectual Curiosity: Keeps up with rapidly evolving AI research and applies learnings pragmatically.
• Problem Solving: Approaches ambiguous problems with structured, data-driven thinking.
• Ownership: Takes full accountability for model quality, timelines, and production reliability.
• Collaboration: Works effectively in cross-functional teams with diverse skill sets.
• Communication: Distils complex technical concepts for diverse audiences.
• Adaptability: Thrives in a fast-paced environment where priorities evolve with research breakthroughs.

About the Role

We are looking for a highly analytical and detail-oriented Sales Operations Analyst to join our team. This role is ideal for a candidate with an engineering background, strong problem-solving skills, and experience in CRM management, sales processes, data analysis, and automation. You will work closely with the RevOps leaders to analyze business processes, optimize sales and CRM workflows, and drive data-driven decision-making.

Location: Bangalore
Experience: 3-5 years

Key Responsibilities

• Assess current business and CRM processes, identify gaps, and recommend improvements
• Automate workflows and integrate tools using APIs and automation platforms like Zapier
• Own end to end CRM and Techstack management making sure GTM teams run smoothly and Techstack is cost optimized
• Analyze large, complex datasets to generate actionable sales and business operations insights.
• Collaborate with sales, marketing, and operations teams to enhance efficiency and effectiveness of GTM functioning
• Account and lead list creation and Data Enrichment and for outbound outreach

Mandatory Requirements

• 3-5 years of experience in a data-driven analytical role (preferably in RevOps / SalesOps)
• Engineering background (Computer Science, IT, or related fields preferred).
• Hands-on experience with Salesforce CRM and preferred Hubspot CRM
• Proficiency in Microsoft Office Suite (Excel, PowerPoint, etc.)
• Familiarity with Power BI or similar reporting tools

Preferred Skills

• Knowledge of sales and SaaS sales processes.
• Experience with SQL, Python, and Google Apps Script (JavaScript) for data analysis and automation.
• Exposure to data pipelines and API integrations.
• Experience with lead enrichment tools like Zoominfo, Apollo, Clay, etc.
• Familiarity with analytics tools and reporting dashboards.

#LI-PB1

About the Role

We are looking for a Lead – Data, RevOps to drive data-backed decision-making across our GTM and Revenue Operations teams.

This role sits at the intersection of data and business and requires someone who comes with a builder mindset to any problem statement thrown at them and scales, maintains and improves these new and existing systems

You will work closely with the growth team leadership and cross-functional teams to ensure that data is insightful, accurate, accessible, and actionable.

Roles & Responsibilities

• Own and manage business-critical data across Revenue, Operations, and GTM functions
• Design and build dashboards and reports using BI tools (e.g., Power BI) to enable real-time visibility into key metrics
• Analyze business performance across pipeline, revenue, and operational metrics to generate actionable insights
• Partner with cross-functional teams (Sales, Marketing, Customer Success, Finance) to support data-driven decision making
• Translate business problems into structured data analysis and scalable reporting solutions
• Automate recurring reporting and workflows using tools like Excel, SQL, and Python to improve efficiency
• Develop and maintain data models and ensure consistency across reporting systems
• Identify gaps in processes and recommend data-backed improvements to enhance operational efficiency
• Support planning and forecasting through data analysis and performance tracking
• Ensure data accuracy, hygiene, and governance across systems and dashboards
• Contribute to building scalable data infrastructure and improving accessibility of insights across teams
• Present insights and recommendations clearly to stakeholders, including leadership
• Lead and manage a team of data analysts, market research, and sales ops ICs

What We’re Looking For

Experience

• 4 - 8 years of experience in Data Analytics, Business Analytics, or Revenue Operations
• Prior experience working with Sales / GTM / Revenue teams is preferred
• Experience is leading a team of analysts is preferred

Technical Skills

• Strong proficiency in SQL
• Hands-on experience with Power BI (or similar BI tools) – Advanced level
• Strong working knowledge of Excel / Google Sheets
• Experience with Python for automation and data processing (preferred)
• Familiarity with data warehouses (e.g., BigQuery, Snowflake, Redshift)

Business & Analytical Skills

• Strong understanding of business metrics across pipeline, revenue, and operations
• Ability to convert ambiguous business problems into structured analysis
• Strong problem-solving and analytical thinking

Soft Skills & Mindset

• High ownership and accountability
• Ability to work in a fast-paced, cross-functional environment
• Strong communication skills, with the ability to work with senior stakeholders

Good to Have

• Experience in B2B SaaS or high-growth startup environments
• Exposure to CRM tools (e.g., HubSpot, Salesforce)
• Experience working on performance tracking, forecasting, or incentive reporting

#LI-PB1

About the Role

We are looking for a highly analytical and detail-oriented Sales Operations Analyst to join our team. This role is ideal for a candidate with an engineering background, strong problem-solving skills, and experience in CRM management, sales processes, data analysis, and automation. You will work closely with the RevOps leaders to analyze business processes, optimize sales and CRM workflows, and drive data-driven decision-making.

Location: Gurgaon
Experience: 3-5 years

Key Responsibilities

• Assess current business and CRM processes, identify gaps, and recommend improvements
• Automate workflows and integrate tools using APIs and automation platforms like Zapier
• Own end to end CRM and Techstack management making sure GTM teams run smoothly and Techstack is cost optimized
• Analyze large, complex datasets to generate actionable sales and business operations insights.
• Collaborate with sales, marketing, and operations teams to enhance efficiency and effectiveness of GTM functioning
• Account and lead list creation and Data Enrichment and for outbound outreach

Mandatory Requirements

• 3-5 years of experience in a data-driven analytical role (preferably in RevOps / SalesOps)
• Engineering background (Computer Science, IT, or related fields preferred).
• Hands-on experience with Salesforce CRM and preferred Hubspot CRM
• Proficiency in Microsoft Office Suite (Excel, PowerPoint, etc.)
• Familiarity with Power BI or similar reporting tools

Preferred Skills

• Knowledge of sales and SaaS sales processes.
• Experience with SQL, Python, and Google Apps Script (JavaScript) for data analysis and automation.
• Exposure to data pipelines and API integrations.
• Experience with lead enrichment tools like Zoominfo, Apollo, Clay, etc.
• Familiarity with analytics tools and reporting dashboards.

#LI-PB1