Who we are:

Financeit is a point-of-sale financing provider serving some of the largest home improvement and retail organizations in Canada. Our platform helps businesses close more sales by offering customers affordable monthly payment options for their next big home improvement, vehicle or retail purchase.

We are small enough that you can make an impact within the company and large enough to make an impact in the market. Financeit is a company where collaboration, inclusivity, fairness, and respect aren’t just ideas that get talked about, but are part of who we are. If such a workplace intrigues you, we hope you’ll join us.

About the role:

Do you love diving into data, solving problems with tech, and learning how fintechs get funded by lenders? Are you equally skilled at balancing deep thinking with pragmatic execution, and always delivering on time? If you’re ready to take your strategic thinking, advanced analytic skills, and knack for digging into the details to the next level, while leaving your mark on a rapidly growing organization, we want to hear from you!

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.

We are seeking an experienced Data Engineer to join our data team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data integration processes, and data infrastructure on AWS cloud. You will collaborate closely with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization.

Requirements

Key Responsibilities:

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

Tiger Analytics is looking for an experienced Lead Data Engineer to design, build, and optimize scalable data platforms and real-time data solutions. The ideal candidate will have strong hands-on expertise in Python, SQL, cloud platforms, distributed data processing, streaming technologies, and modern cloud data warehouses.

Key Responsibilities:

We are looking for a Senior Data Engineer with a strong software engineering background and deep expertise in building modern cloud-based data platforms.

You will be responsible for designing and developing scalable data infrastructure, building reliable data pipelines, and establishing engineering best practices across our data ecosystem. This role is ideal for someone who enjoys solving complex engineering challenges while enabling analytics, machine learning, and product teams with high-quality data.

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

Tiger Analytics is looking for an experienced Data Engineer to design, build, and operate large-scale batch and real-time data pipelines that power enterprise data and analytics platforms.

You will work closely with Agile engineering, architecture, and product teams to build reliable, scalable data solutions spanning data ingestion, transformation, orchestration, event streaming, and downstream data delivery. The ideal candidate is someone who enjoys solving complex data engineering problems and building highly available systems that operate at enterprise scale.

Key Responsibilities:

About Us

For more than a decade, Zyte has led the way in building powerful, easy-to-use tools to collect, format and deliver web data, quickly, dependably and at scale. And today, the data we extract helps thousands of organisations make smarter business decisions, secure competitive advantage, and drive sustainable growth. Today, over 3,000 companies and 1 million developers rely on our tools and services to get the data they need from the web.

About Customer Operations

Customer Operations is the function responsible for the end-to-end operational success of Zyte’s client base, from the moment a customer signs to the ongoing health and performance of Zytes services. The team sits at the intersection of client relationship management, technical delivery and operational excellence. The Service Delivery Manager role sits in the second of these tracks, working closely with the Project Manager team as well as Tech, QA, Product and Commercial teams across the business.

The Service Delivery Manager is the single point of accountability, within their portfolio, for client operations at Zyte, bridging the gap between customer needs and technical delivery. By owning the full service relationship — from day-to-day operations to escalations and KPIs — the SDM ensures reliable, high-quality data services while driving the consistency and trust that underpin long-term client retention.

Tiger Analytics is a leading advanced analytics consulting firm specializing in AI and machine learning. We are recognized as the trusted analytics partner for several Fortune 100 companies, enabling them to generate significant business value from their data. Our team combines industry expertise with data science skills to deliver comprehensive analytics solutions.

We are on a mission to build the best analytics consulting team, and we are currently looking for a specialized Salesforce Developer to join our dynamic team. In this role, you will be the driving force behind our marketing automation and integration efforts. You will focus primarily on designing, building, and supporting complex architectures within Salesforce Marketing Cloud (SFMC) and ensuring seamless data flow between our front-end applications, CRM, and external platforms.

Key Responsibilities

Who we are:

Financeit is a point-of-sale financing provider serving some of the largest home improvement and retail organizations in Canada. Our platform helps businesses close more sales by offering customers affordable monthly payment options for their next big home improvement, vehicle or retail purchase.

We are small enough that you can make an impact within the company and large enough to make an impact in the market. Financeit is a company where collaboration, inclusivity, fairness, and respect aren’t just ideas that get talked about, but are part of who we are. If such a workplace intrigues you, we hope you’ll join us.

About the role:

Do you love working with multi-disciplinary teams and stakeholders across a wide range of functions to implement and solve data engineering challenges? Are you equally skilled at delivering simple solutions to complex problems and consistently delivering on time? 

We are looking for a skilled and detail-oriented Data Engineer to join our growing data team. In this role, you will be responsible for building and maintaining the data pipelines, models, and infrastructure that deliver reliable, high-quality data to teams across the organization. You will work closely with business stakeholders to ensure our data warehouse is accurate, well-documented, and scalable as the business grows.

What you'll be doing:

About Us

For more than a decade, Zyte has led the way in building powerful, easy-to-use tools to collect, format and deliver web data, quickly, dependably and at scale. And today, the data we extract helps thousands of organisations make smarter business decisions, secure competitive advantage, and drive sustainable growth. Today, over 3,000 companies and 1 million developers rely on our tools and services to get the data they need from the web.

About Customer Operations

Customer Operations is the function responsible for the end-to-end operational success of Zyte’s client base, from the moment a customer signs to the ongoing health and performance of Zytes services. The team sits at the intersection of client relationship management, technical delivery and operational excellence. The Service Delivery Manager role sits in the second of these tracks, working closely with the Project Manager team as well as Tech, QA, Product and Commercial teams across the business.

The Service Delivery Manager is the single point of accountability, within their portfolio, for client operations at Zyte, bridging the gap between customer needs and technical delivery. By owning the full service relationship — from day-to-day operations to escalations and KPIs — the SDM ensures reliable, high-quality data services while driving the consistency and trust that underpin long-term client retention.

Location: New York, United States (Office) - Must be able to work office based in NYC

On-Site | Full-time

Compensation: $300K - $375K

Our client, a premier venture accelerator backing high-growth technology and emerging-tech startups, is seeking an experienced and self-directed Senior Machine Learning Engineer to join their in-house engineering team in New York City.

Reporting directly to the Chief Technology Officer (CTO), the Senior Machine Learning Engineer will take full ownership of features throughout the product lifecycle—from requirements definition to production deployment. This onsite role requires an entrepreneurial mindset and deep technical execution to turn loosely defined problems into robust, production-ready machine learning and LLM-based systems without reliance on large engineering teams or dedicated project managers.

Key Responsibilities