Senior / Staff Data Engineer | AI, Data Platforms & Data Products

Jobgether; United States; Remote; USD 150,000 - 220,000; Full time

Benefits offered by this job

Equity participation

Fully remote (Canada)

Impactful data engineering leadership

Autonomy and growth

Job summary

Jobgether is seeking a senior data engineer to design and scale cloud-based data infrastructure and analytics platforms. You will build reliable data pipelines, event-driven systems, and AI-enabled capabilities, collaborating across AWS, Snowflake, Databricks, Spark, Airflow, and dbt to deliver scalable, production-grade data solutions.

You will own the deployment lifecycle, mentor engineers, and help shape architecture and governance while delivering measurable business impact.

Qualifications

• 5+ years of professional engineering experience with technical ownership valued highly.
• Experience building data infrastructure, platforms, or data products from scratch.
• Strong foundations in distributed systems, platform engineering, or modern data engineering.
• Hands-on cloud experience (AWS or equivalent).
• Familiarity with Snowflake, Databricks, Spark, Airflow, and dbt.

Responsibilities

• Design, build, and evolve cloud-based data infrastructure and scalable data platforms.
• Develop reliable data pipelines, distributed systems, and event-driven architectures.
• Work with AWS, Snowflake, Databricks, Spark, Airflow, and dbt to create data solutions.
• Develop AI-enabled data capabilities, including LLM-powered workflows and semantic layers.
• Build self-service analytics platforms and customer-facing data products.
• Address governance, metadata, and knowledge management challenges.
• Evaluate new technologies to improve the data platform.
• Own systems from deployment to on-call support and reliability.
• Define architectural direction and engineering standards.

Skills

Cloud data platforms

Distributed systems

AWS

Snowflake

Databricks

Spark

Airflow

dbt

AI-enabled analytics

Self-service analytics

Production ownership

Architecture leadership

Mentoring

Tools

Python

SQL

Job description

Accountabilities

• Design, build, and evolve modern cloud-based data infrastructure and scalable data platforms from the ground up.
• Develop reliable data pipelines, distributed systems, and event-driven architectures that support business-critical applications and products.
• Work across technologies such as AWS, Snowflake, Databricks, Spark, Airflow, and dbt to create robust and maintainable data solutions.
• Develop AI-enabled data capabilities, including LLM-powered workflows, agentic data solutions, semantic layers, and natural-language analytics experiences.
• Build self-service analytics platforms and customer-facing or embedded data products that improve product adoption, customer experience, and commercial performance.
• Address complex challenges involving data streaming, governance, metadata management, and knowledge systems.
• Evaluate emerging technologies and identify opportunities to improve the data platform as the AI and data ecosystem evolves.
• Own systems throughout their lifecycle, including deployment, production reliability, observability, performance monitoring, incident response, and on-call support.
• Define architectural direction, establish engineering standards, and make informed technical decisions that support long-term scalability.
• Collaborate with technical leadership and cross-functional teams to identify high-impact problems, prioritize solutions, and connect engineering investments to business outcomes.
• Mentor other engineers, share technical expertise, and help establish and grow the data engineering function.

Requirements

• At least 5 years of professional engineering experience, with demonstrated achievements and technical ownership valued more highly than tenure alone.
• Proven experience building data infrastructure, platforms, or data products from scratch, ideally as an early engineering hire or within a startup or scale-up environment.
• Strong foundations in distributed systems, platform engineering, software engineering, or modern data engineering.
• Hands-on experience with cloud infrastructure, particularly AWS or an equivalent cloud platform.
• Familiarity with modern data technologies such as Snowflake, Databricks, Spark, Airflow, and dbt.
• Experience designing streaming or event-driven architectures and building scalable, reliable production systems.
• Demonstrated ownership of production environments, including monitoring, observability, troubleshooting, incident response, and on-call responsibilities.
• Experience with AI and large language models in data platforms, including AI-assisted analytics, semantic layers, AI data assistants, or natural-language interfaces, is highly desirable.
• Experience developing self-service analytics capabilities, embedded analytics, or customer-facing data products.
• A track record of delivering measurable technical or commercial impact, such as generating revenue, reducing costs, improving customer experience, or increasing operational efficiency.
• For Staff-level candidates, the ability to lead architecture decisions, influence technical direction, establish engineering practices, and mentor other engineers.
• Strong product thinking, commercial awareness, analytical problem-solving skills, and the ability to communicate and justify complex technical decisions.
• Comfortable operating autonomously in ambiguous environments, identifying important problems, designing solutions, and taking responsibility for outcomes from conception through production.
• A background in software engineering, data engineering, machine learning, or quantitative disciplines may be particularly relevant.
• Work authorization: Must already have unrestricted authorization to work in Canada. Visa sponsorship and visa transfer are not available.

Benefits

• Competitive salary aligned with experience and seniority; the specific salary range has not been disclosed.
• Equity participation, offering an opportunity to share in the company's growth.
• Fully remote work for eligible candidates based in Canada.
• Significant ownership and influence over data platform architecture, engineering strategy, and AI-powered product development.
• The opportunity to help establish and shape a growing data engineering function rather than simply maintain an existing platform.
• Exposure to complex technical challenges spanning cloud infrastructure, distributed systems, AI, analytics, and customer-facing data products.
• Direct collaboration with experienced technical leadership and the opportunity to influence high-impact business decisions.
• An environment that values autonomy, experimentation, innovation, and measurable outcomes.
• Healthcare coverage, paid leave, retirement benefits, and other specific employment perks were not detailed in the provided job description.