Job description

WHO WE ARE: Splice is a creative platform for people who make music. Serious producers choose Splice Sounds to bring their ideas to life. A subscription to Splice inspires and accelerates creative success for digital music creators with an industry-leading catalog of sounds and samples and an expanding AI stack. With a rent-to-own marketplace of DAWs and plugins, the Splice experience seamlessly integrates into any music production workflow, regardless of DAW (Digital Audio Workstation). Via Splice, an unparalleled team of sound designers and sample creators are fueling the success of a growing global community of chart-topping producers, students, and DIY creators. HOW WE WORK: At Splice, DISCO is a rallying cry for collaboration, accountability, and unity within our organization; Direct, Inclusive, Splice Together, Creator Centric, and Optimistic. Our shared success depends on our ability to support one another, work well together, and communicate directly. By embracing flexibility and a unified approach, we can navigate anything that’s thrown at us. Splice embraces a culture of remote work. You’ll see your colleagues showing up from across the US and the UK. In order to keep us working well as a team, we have regular communication, including Town Halls, departmental all-hands and get-togethers. When you join Splice, you join a network of colleagues, peers, and collaborators. Are you ready? JOB TITLE: Data Engineer II LOCATION: REMOTE- US THE ROLE: We're looking for a Data Engineer II to join Splice's Data Engineering team and help scale the platform that powers creator payouts, revenue reporting, and product analytics across Splice's product lines. Data Engineering builds and maintains the reliable foundation that makes trusted data possible, enabling teams across Splice to work from a governed, consistent source of truth. We are accountable for the warehouse and pipeline foundation; ingestion, transformations, and the standards and quality guardrails that keep data consistent, timely, and usable. This is a mid-level, high-ownership role: engineers here carry features from technical design through delivery, monitoring, and documentation, and participate in our on-call rotation. The work requires hands-on data engineering experience. Our stack; BigQuery, SQLMesh, Python, Dagster- is distinct from Splice's web engineering stack, and effective contribution requires domain expertise in data pipeline systems, not general software engineering background. TEAM INFORMATION: You'll be joining Splice's Data Engineering team, a small, embedded platform team responsible for the pipelines, transformations, and warehouse infrastructure the rest of the company depends on. Our systems support finance-critical processes (contributor payouts, GAAP revenue reporting), product analytics, and data reliability for customer-facing features like recommendations. We operate with a shared on-call rotation and a strong bias toward observable, well-documented, durable systems. WHAT YOU’LL DO: Pipeline Development & Transformation Build and maintain data models in SQLMesh, including incremental strategies, testing, and documentation Continuously improve and evolve our orchestration and transformation infrastructure as the platform scales Modernize legacy pipeline jobs in Python and SQLMesh, reducing technical debt over time Reliability & Observability Participate in on-call rotation; triage and resolve incidents including ETL failures, data unavailability, and core metric breakage Build introspectable software: define alert thresholds, emit metrics and logs, produce dashboards, and establish closed-loop escalation paths Improve observability across the platform so failures are caught early and resolved quickly Finance & Revenue Pipelines Extend and maintain pipelines powering creator payouts and GAAP-compliant revenue reporting across Splice's product lines Build pipeline support for new revenue streams as Splice launches them Platform Quality & Optimization Improve query performance and reduce compute costs in BigQuery Contribute to ingestion platform work and product analytics infrastructure Uphold our definition of done: validation complete, documentation updated, monitoring in place before closing a ticket JOB REQUIREMENTS: 3+ years of hands-on data engineering experience in a production environment Proficiency in SQL — window functions, CTEs, query optimization, execution plan analysis Proficiency in Python for pipeline logic, data transformation, and testing (maintainable code, not just scripts) Experience with a modern transformation framework — SQLMesh, dbt, or equivalent — including incremental models, testing, and documentation Production experience with a cloud data warehouse (we use BigQuery); understands partitioning, clustering, and cost management Experience with at least one workflow orchestrator (Dagster, Airflow, Prefect, or equivalent) Demonstrated ability to build observable, reliable data systems — alerting, dashboards, and data quality checks Comfortable owning small-to-medium features from technical design through delivery with limited guidance SPLICE BENEFITS: Compensation & Equity: Competitive pay with annual reviews and equity opportunities. Time Off: Unlimited PTO to recharge and thrive. Health & Wellness: Comprehensive medical, dental, and vision coverage for you and your dependents. Retirement Savings: 401(k) plan with immediate vesting and company match. Parental Support: 12 weeks of fully paid parental leave for non-birthing parents, and 18-20 weeks for birthing parents. Flexible Work Options: Work remotely or connect at our office hubs and creative spaces worldwide. Professional Growth: Annual learning budget, leadership programs, and team ambassador opportunities. Community & Connection: Inclusive events, team meet-ups, and vibrant Employee Resource Groups. The national pay range for this role is $94,952 - $118,690. Individual compensation will be commensurate with the candidate's experience. Splice is an Equal Opportunity Employer Splice provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

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Must-haves

We're looking for a Data Engineer II to join Splice's Data Engineering team and help scale the platform that powers creator payouts, revenue reporting, and product analytics across Splice's product lines.
Pipeline Development & Transformation
Build and maintain data models in SQLMesh, including incremental strategies, testing, and documentation
Continuously improve and evolve our orchestration and transformation infrastructure as the platform scales
Modernize legacy pipeline jobs in Python and SQLMesh, reducing technical debt over time
Reliability & Observability
Participate in on-call rotation; triage and resolve incidents including ETL failures, data unavailability, and core metric breakage
Build introspectable software: define alert thresholds, emit metrics and logs, produce dashboards, and establish closed-loop escalation paths
Improve observability across the platform so failures are caught early and resolved quickly
Finance & Revenue Pipelines
Extend and maintain pipelines powering creator payouts and GAAP-compliant revenue reporting across Splice's product lines
Build pipeline support for new revenue streams as Splice launches them
Platform Quality & Optimization
Improve query performance and reduce compute costs in BigQuery
Contribute to ingestion platform work and product analytics infrastructure
Uphold our definition of done: validation complete, documentation updated, monitoring in place before closing a ticket
3+ years of hands-on data engineering experience in a production environment
Proficiency in SQL — window functions, CTEs, query optimization, execution plan analysis
Proficiency in Python for pipeline logic, data transformation, and testing (maintainable code, not just scripts)
Experience with a modern transformation framework — SQLMesh, dbt, or equivalent — including incremental models, testing, and documentation
Production experience with a cloud data warehouse (we use BigQuery); understands partitioning, clustering, and cost management
Experience with at least one workflow orchestrator (Dagster, Airflow, Prefect, or equivalent)
Demonstrated ability to build observable, reliable data systems — alerting, dashboards, and data quality checks
Comfortable owning small-to-medium features from technical design through delivery with limited guidance

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