Were looking for a Data Platform Engineer to join the team responsible for the infrastructure, frameworks, and tools that power data and analytics across the company. In this role, youll help build and maintain the shared platform that data engineers, analytics engineers, data scientists, and ML engineers depend on every day. Youll work closely with senior platform engineers, learning how to build and operate large-scale data infrastructure while taking on real ownership of platform components as you grow. This is a phenomenal opportunity for someone early in their career who is passionate about data infrastructure, cloud platforms, and helping others do their best work.

What Youll Do:

Platform Engineering & Support

• Build and maintain shared platform infrastructure, including data pipeline frameworks, reusable templates, and developer tooling.
• Support the administration and day-to-day operations of our cloud data platforms (Databricks and Snowflake), including monitoring, access management, and resolving performance problems.
• Help implement and maintain infrastructure-as-code (Terraform) for data platform components, following established standards and patterns.
• Assist in evaluating new tools and frameworks, conducting research and proof-of-concepts to inform platform decisions.

Data Warehouse & Analytics Infrastructure

• Support the optimization and governance of our Databricks and Snowflake environments, including cost monitoring and query performance analysis.
• Help maintain and improve platform capabilities - access patterns, environment configurations, and shared tooling - that enable data and analytics engineers and data scientists to build effectively.
• Partner with analytics and BI teams to solve data platform issues and ensure data products remain accurate and accessible.

Streaming & Pipeline Infrastructure

• Support the maintenance and monitoring of streaming infrastructure (Kafka and related tooling), surfacing issues and delivering fixes with mentorship from senior engineers.
• Assist in implementing and testing pipeline framework components that enable other teams to build reliable batch and streaming workloads.

Platform Reliability & Operations

• Monitor platform health, respond to alerts, and participate in incident response for data platform issues under the guidance of senior engineers.
• Write and maintain documentation, runbooks, and operational guides that help the broader team maintain platform reliability.
• Participate in post-mortems and help implement follow-up improvements.

Growth & Collaboration

• Work closely with senior and principal platform engineers to learn platform architecture patterns and standard methodologies.
• Collaborate with data engineering, analytics, and ML teams to understand their needs and help translate them into platform improvements.
• Participate in code reviews and uphold high standards of quality and continuous improvement.

What Youll Bring:

Required

• 2-4 years of experience in data engineering, software engineering, or a related technical role.
• Familiarity with and hands-on exposure to cloud-based data platforms.
• Solid understanding of SQL and proficiency in at least one programming language used in data engineering (Python preferred).
• Basic understanding of data pipeline concepts - batch processing, scheduling, data modeling - and a desire to deepen that knowledge.
• Some exposure to infrastructure-as-code concepts; hands-on experience with Terraform is a plus.
• Familiarity with Git-based workflows and an understanding of CI/CD fundamentals (GitHub Actions, GitLab CI, or equivalent).
• Curiosity, a growth mindset, and a track record of learning new technical skills quickly.
• Ability to share information clearly and work together across technical teams.

Nice to Have

• Exposure to streaming concepts or tools (Kafka, Kinesis, or similar).
• Hands-on experience with Databricks or Snowflake is a strong plus.
• Familiarity with modern data stack tooling: transformation, orchestration, and data catalogs.
• Experience with cloud environments (AWS or Azure preferred).
• Awareness of MLOps or LLM Ops concepts and the data infrastructure needs of ML teams.
• Background in or exposure to data governance, data quality, or observability tooling.

What Success Looks Like:

In your first 90 days, youll have a proven understanding of our data platform stack, completed onboarding to key tools and systems, and made your first contributions to platform components. Within six months, youll be independently owning small platform tasks, actively contributing to team projects, and building the skills and context needed to take on increasing responsibility over time.

The pay transparency range for this role is listed below. The hiring range will vary based on factors such as experience, skills, location and market conditions. Additionally, employees may be eligible for annual and long-term incentives as part of their overall compensation package.

#Wizards

The pay transparency range for this role is listed below. The hiring range will vary based on factors such as experience, skills, location and market conditions. Additionally, employees may be eligible for annual and long-term incentives as part of their overall compensation package.

Pay Range

$108,300-$162,500 USD

Employees may be eligible for annual and long-term incentives as part of their overall compensation package, depending on role, location, and eligibility. Benefits and programs may include:

• Health & Wellness
• Time Off to Recharge
• Financial Well-being
• Life & Family Support
• Volunteer and Community Initiatives
• Learning & Development
• Exclusive Perks