Staff Data Engineer

Company Overview

Arccos is the global leader in Artificial Intelligence and Connected Golf technologies. By combining advanced data analytics, cloud computing, and machine learning, we deliver powerful insights that help golfers of all skill levels improve faster and enjoy the game more.

As golf's first A.I. platform, Arccos automatically tracks every shot and provides personalized Strokes Gained analytics, smart club distances, and A.I.-powered caddie recommendations for any course in the world. Our ecosystem integrates industry-leading hardware and software, supported by a global manufacturing and distribution footprint that ensures golfers everywhere can benefit from our technology.

Through the Arccos Dashboard, players and instructors gain deep performance insights across every club, shot, hole, and round—fueling smarter practice and better on-course decisions.

The Opportunity

Our data volume, structure, and business logic have grown faster than our underlying architecture. Today, working with our data requires deep tribal knowledge, significant time investment, and complex queries across scattered tables and pipelines.

We are hiring a Staff Data Engineer, Data Architecture — an individual contributor who will take end-to-end ownership of designing, documenting, and implementing the next-generation data architecture for Arccos. This is a rare opportunity to start with near-greenfield autonomy, rethinking ingestion, modeling, transformations, governance, and access patterns across the entire company.

This role is for someone who loves building systems right, turning chaos into clarity, and enabling data science, analytics, and product teams to move dramatically faster.

What You'll Do

Architect and Implement a Modern, Unified Data Platform

• Redesign Arccos's data architecture from the ground up with a focus on scalability, clarity, and long-term maintainability.

• Create canonical models, schemas, and semantic layers that eliminate duplicated logic and reduce time-to-insight across teams.

• Unify fragmented tables and data sources into coherent, well-documented, lineage-aware structures.

• Identify where to shift business logic upstream and create reliable, auditable data transformations.

Design a Data Ecosystem that Supports AI-Driven Analytics

• Architect data structures, metadata layers, and documentation standards that enable AI tooling, including Snowflake's Conversational Analytics and emerging agentic analysis workflows.

• Build and maintain a comprehensive, AI-ready data dictionary across MySQL and Snowflake — ensuring every table and field is clearly described, contextualized, and optimized for LLM-based context retrieval.

• Ensure schemas, lineage, semantic layers, and business logic are organized so AI systems can reliably understand context, meaning, and relationships across datasets.

• Provide data architecture guidance and logging infrastructure to support Arccos's LLM/AI strategy.

• Build with the assumption that a growing percentage of internal analytics, querying, and business insights will be generated or augmented by AI agents.

Build High-Quality Data Pipelines

• Own ingestion and transformation pipelines spanning: app telemetry, shot and sensor data, subscriptions and commerce, internal business systems, and product analytics.

• Architect highly optimized Snowflake models and performance-tuned warehouse patterns.

• Improve organization and access patterns across large volumes of raw and enriched S3 data, including flattening and restructuring JSON dumps and semi-structured data.

• Use (or refine) existing orchestration tooling like Airflow; recommend improvements where appropriate.

Create Standards, Documentation, and Governance

• Define naming conventions, folder structures, transformation standards, and SQL style guidelines.

• Build and maintain comprehensive data documentation, lineage mapping, and data dictionaries.

• Introduce robust data quality checks, automated validation, and monitoring.

Partner Across the Organization

• Work closely with data science, engineering, product, and growth teams to create data structures that support analytics, ML, and product development.

• Contribute to active feature development in parallel with onboarding — ensuring new data structures are designed correctly from the start rather than retrofitted later.

• Dramatically simplify the downstream query experience so teams can be productive without heroic effort.

• Be the company-wide steward of best practices for ingesting, modeling, and deploying data.

What We're Looking For

Required

• 6–12+ years in data engineering or data architecture roles.

• Demonstrated experience architecting both transactional database systems and data warehouses — understanding how data flows from OLTP sources into analytical environments and designing both sides well.

• Deep experience with:

• AWS (S3, Lambda, IAM, Airflow)

• Snowflake (performance tuning, modeling, optimization)

• MySQL (schema design, query optimization, managing production OLTP data as a source for analytics)

• Python for ETL/ELT development

• SQL expertise at a high craft level.

• Proven success architecting complex, multi-source data ecosystems.

• Experience working with large semi-structured datasets (JSON, Parquet, logs).

• Demonstrated ability to bring order to fragmented, legacy, or fast-evolving environments.

• Understanding of how metadata, documentation, and schema design influence LLM performance and context retrieval.

• Strong communication and documentation skills.

• Thrives as a high-ownership, self-directed individual contributor who can assess what needs to be done and drive toward it autonomously.

Nice to Have

• Experience with AI-driven analytics and agentic tools (e.g., Snowflake's Conversational Analytics).

• Ability to architect data systems that support natural-language interfaces and automated insight generation.

• Experience with analytical modeling layer tools.

• Experience supporting ML training pipelines (S3 → high-memory/GPU compute).

• Database administration familiarity (replication, uptime management, performance troubleshooting) — this role is not a DBA, but comfort interfacing with outsourced DBA resources is a plus.

• Familiarity with MySQL schema migration strategies.

• Interest in golf or sports data (not required).

Who You Are

• You're a systems thinker who enjoys building elegant, maintainable architectures.

• You care deeply about data integrity.

• You love designing, building, coding, optimizing, and documenting.

• You take pride in clarity and structure.

• You lean toward doing things right, not just doing them fast.

• You enjoy partnering with data science and engineering teams to remove friction and unlock their potential.

Success in the First 90–180 Days

Within 30 Days

• Develop a thorough understanding of Arccos's current data landscape — MySQL schemas, Snowflake warehouse, S3 data, pipelines, and key pain points.

• Produce a documented assessment of the ecosystem: gaps, risks, duplication, and technical debt.

• Begin contributing to active feature development and new initiatives (e.g., LLM/AI logging and data architecture needs) in parallel with onboarding.

Within 60 Days

• Deliver a proposed Target State Architecture with clear principles and a phased implementation plan.

• Deliver an AI-ready data dictionary covering key MySQL and Snowflake tables — every table and field documented with clear descriptions optimized for LLM consumption.

Within 90 Days

• Stand up initial canonical models for key domains (e.g., users, sessions, commerce, shot-level data).

• Establish naming conventions, transformation standards, and folder structures.

• Deliver initial documentation and lineage mapping for top critical pipelines.

• Reduce query complexity for high-impact stakeholders (DS, product, exec analytics).

Within 180 Days

• Implement major portions of the redesigned ingest + transformation pipeline.

• Migrate priority data sources to new canonical models.

• Introduce automated data quality checks and monitoring across core domains.

• Measurably reduce analysis time and eliminate major areas of schema confusion.

• Deliver a documented, scalable architecture that supports ML, analytics, and product needs.

• Be recognized internally as the owner and subject matter expert for Arccos's data platform.

What We Offer

• Competitive total compensation (base + equity) aligned to staff-level scope

• Medical, Dental, and Vision insurance

• 401(k) program with company match

• Golf benefits—rounds reimbursed for testing and occasional travel to top courses for team events

• A chance to make a meaningful impact as we scale and grow

Equal Opportunity Employer

Arccos is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected characteristic.

How to Apply

Interested candidates should send their resume and a brief cover letter to [email protected] .