IT Data Analytics Specialist

On-siteMidData Engineerposted 3d ago

✦Role summaryAI-generated

This role focuses on building and maintaining data products and self‑service tools on Snowflake, bridging data engineering and full‑stack development. You will collaborate with data engineers, product managers, and stakeholders to design scalable platforms that make data more accessible across the organization.

About this role

We are looking for a talented and driven Full-Stack Engineer to join our Data Products Enablement team. In this role, you will be at the intersection of modern data engineering and full-stack application development, building the platforms, tools, and self-service interfaces that empower our internal data consumers.

You will work closely with data engineers, analytics engineers, product managers, and business stakeholders to design, build, and maintain scalable data products powered by Snowflake. Your work will directly influence how teams across the organization discover, access, trust, and act on data.

This is a high-impact, hands-on engineering role ideal for someone who thrives at the crossroads of data infrastructure and software engineering, and who is passionate about building products that make data radically more accessible

Key Responsibilities

Platform & Product Development

Design and develop full-stack data product features, including front-end dashboards, APIs, and back-end data pipelines, integrating seamlessly with Snowflake as the core data platform.
Build and maintain internal self-service data portals and catalogues that enable business users to explore, request, and consume certified datasets without engineering intervention.
Architect RESTful and GraphQL APIs that surface Snowflake data assets to front-end applications, partner systems, and embedded analytics tools.
Develop reusable UI components and micro-frontends using modern JavaScript frameworks, adhering to accessibility and performance best practices.

Snowflake & Data Engineering

Collaborate on the design of data contracts and semantic layer definitions to provide consistent, trusted metrics to downstream consumers.

DevOps, Quality & Collaboration

Champion CI/CD best practices for both application and data code, maintaining robust automated test coverage (unit, integration, and end-to-end).
Participate actively in Agile ceremonies, contribute to sprint planning, and drive engineering excellence through code reviews, pair programming, and technical documentation.
Monitor production systems, triage incidents, and perform root-cause analysis, ensuring high availability and SLA compliance across data product services.
Mentor junior engineers and contribute to the team's technical knowledge base through design documents, runbooks, and internal tech talks.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering, or a related technical discipline.
4–8 years of professional experience in software engineering, with at least 2 years directly involving data platform or data product development.
Demonstrated track record of delivering production-grade, full-stack applications with measurable business impact.
Proficiency in advanced SQL (window functions, CTEs, recursive queries, QUALIFY) and Snowflake-specific DDL/DML including Time Travel, Zero-Copy Cloning, and Streams & Tasks.
Solid understanding of Snowflake's multi-cluster architecture, virtual warehouse sizing/scaling policies, query profiling, and cost governance via resource monitors.
Front-End: Strong proficiency in React.js (with hooks and context) or Vue.js, TypeScript, and a working knowledge of state management libraries (Redux / Pinia / Zustand).
Back-End: Hands-on experience building production APIs with Python (FastAPI / Flask / Django REST Framework) or Node.js (Express / NestJS).
Databases: Working knowledge of relational databases (PostgreSQL, MySQL) alongside Snowflake, including schema design, query optimization, and ORM usage.
Cloud Platforms: Practical exposure to AWS, Azure, or GCP services (e.g., S3/ADLS/GCS, Lambda/Functions, ECS/AKS/GKE) in the context of data workloads.
Infrastructure & Tooling: Experience with Docker, Kubernetes basics, Terraform or similar IaC tools, and standard CI/CD platforms (GitHub Actions, GitLab CI, Jenkins).