Senior Data Platform Engineer

About SpotOn

We’re not just building restaurant tech, we’re giving independent restaurants the tools to compete and win. From our award-winning point-of-sale to AI-powered profit tools, everything we do helps operators boost profit, work smarter, and keep their best people. And every solution is backed by real humans who actually give a sh*t about helping restaurants succeed.

• Named the #1 Restaurant POS by G2 (Summer 2026), based on ratings from real users

• Rated the top-rated point-of-sale (POS) for restaurants, bars, retail, and small businesses by Capterra users

• Awarded Great Places to Work and Built In’s Best Workplaces for multiple years running

We move fast, care hard, and fight for independent restaurant operators to do what they love, and love doing it. If you’re looking to make an impact with heart and hustle, SpotOn is the place for you.

Senior Platform Engineer - Data (Analytics)

Location: Remote but available to come into the Chicago office occasionally.

The Role

SpotOn's analytics platform is how the company understands its business: how many merchants are live and growing, how much payment volume flows through our platform, how merchants use our products, and how we turn that into revenue. As an Engineer on the Data Platform team, you will own that platform end to end. From ingesting data out of our production systems and third-party tools, through monitoring and job health, to the core dbt models that serve as the source of truth for analysts, finance, product, and power users across the company.

This is a hands-on, high-ownership role. You'll treat data as a product: you know who uses it, you know when it's wrong before they do, and you design models that make the right answer the easy answer. You'll work across a business that spans multiple POS platforms, payments, and SaaS products, so unifying messy, similar-but-not-identical sources into one consistent view is core to the job.

What You'll Do

Ingestion & Pipelines

• Build and operate reliable pipelines that land data from production databases (CDC), SaaS tools, and third-party APIs into Snowflake.

• Orchestrate batch and incremental workloads in Airflow, choosing the right load pattern (full, incremental, CDC) for each source.

• Onboard new sources quickly and repeatably, with clear contracts for schema, freshness, and ownership.

Modeling & Serving

• Design and own the core dbt models (merchants, orders, payments, revenue, and product usage) that downstream teams build on.

• Unify data across platforms and product lines (e.g., legacy and next-generation POS) into consistent, well-documented models so users never have to reason about where the data came from.

• Build curated, analysis-ready marts for finance and product analytics, and enable self-serve for power users through clean semantics and documentation.

• Write reusable macros and conventions that keep a growing dbt project consistent and maintainable.

Data Quality, Monitoring & Job Health

• Define and enforce data tests (uniqueness, nulls, referential integrity, metric reconciliation) as a first-class part of every model.

• Own monitoring and alerting for pipeline health, freshness, and volume anomalies; respond to incidents, run root-cause analysis, and fix the underlying problem.

• Reconcile financial data (processing volume, fees, revenue, billing) against source systems so finance can trust the numbers.

Performance & Cost

• Tune Snowflake and dbt workloads, incremental strategies, clustering, materialization choices, and warehouse sizing to keep models fast and costs predictable.

• Monitor platform spend and usage and recommend changes that pay for themselves.

Partnership & Leadership

• Partner with analysts, finance, product, and engineering teams to translate ambiguous business questions into durable data products.

• Raise the bar on SQL, modeling, and testing practices through code review, documentation, and mentorship.

• Use AI-assisted development tools thoughtfully to move faster, while reviewing their output with the same rigor you'd apply to anyone else's code.

What Success Looks Like

• First 90 days: You own a slice of the pipeline end to end, have shipped improvements to core models, and are a trusted responder when jobs fail.

• First 6-12 months: Core models are better tested and faster, data incidents are caught before stakeholders notice, and Finance and Product teams self-serve on models you designed.

Skills & Experience

Required

• 5+ years of experience in data engineering, analytics engineering, or a closely related role.

• Expert SQL: complex joins, window functions, incremental logic, and query performance tuning.

• Strong Python for pipeline development, automation, and tooling.

• Deep hands-on experience with dbt (models, tests, macros, incremental materializations, documentation) and Snowflake (or a comparable cloud warehouse).

• Strong data modeling fundamentals: dimensional modeling, grain, slowly changing dimensions, and designing models for many downstream consumers.

• Experience building and operating production pipelines with orchestration (Airflow or similar), including monitoring, alerting, and on-call ownership.

• A testing mindset: you build data quality checks in from the start and know when a number is right and when it needs a second look.

• Experience supporting product analytics (event data, funnels, adoption, retention) and/or financial analytics (revenue, payments, reconciliation).

• Clear communication with both technical and non-technical stakeholders.

Nice to Have

• Experience in payments, fintech, POS, or SaaS businesses.

• CDC and streaming ingestion (e.g., Debezium, Kafka, Streamkap) or modern ingestion frameworks (e.g., dlt, Fivetran).

• Unifying data across platform migrations or multiple product backends.

• Snowflake administration: RBAC, cost governance, warehouse management.

• Data observability tools, CI/CD for dbt, and infrastructure as code.

• Experience enabling BI and semantic layers (e.g., Metabase, Looker, dbt Semantic Layer) or AI/agent access to warehouse data.

Compensation:

Our base pay range starts at $130,000 -$173,000/year for this role

Please note the salary range listed is just one component of a competitive compensation package which includes a company stock plan

Offers will be reflective of the candidate’s location and experience.

Benefits:

At SpotOn, we put people above everything else. We’re known for our innovative software and

technology solutions, but we stand out because of the hard-working humans behind the tech.

We can’t take care of our clients without taking care of our employees first, and that’s why we

invest in you with a competitive benefits package which includes:

● Medical, Dental and Vision Insurance

● 401k with company match

● RSUs

● Paid vacation, 10 company holidays, sick time, and volunteer time off

● Employee Resource Groups to build community and inclusion at work

● Monthly cell phone and internet stipend

● Tuition reimbursement for up to $2,000 per calendar year to assist with your professional development

SpotOn is an equal employment opportunity employer. Qualified candidates are considered for employment without regard to race, religion, gender, gender identity, sexual orientation, national origin, age, military or veteran status, disability, or any other characteristic protected by applicable law.
For U.S. based roles, work authorization in the U.S. is required. Visa sponsorship is not available for this role.

SpotOn is an E-Verify company.