As a Senior Data Engineer on the Data Platform team, you will build the data foundation that Lyric's products run on. Data Platform team owns the canonical data model for Lyric's core healthcare data domains, the framework that ingests, transforms, validates, and serves that data across a multi-tenant platform, and the quality guarantees our consuming teams build against. Our stack is Snowflake, Airflow, dbt, and Python. This is a hands-on building role. You will design and deliver pipelines within our unified framework, implement the canonical data model in production, build validation and observability into the data path rather than around it, and help consuming teams get what they need without a bespoke build every time. You will work closely with our Principal Data Engineer, who sets architectural direction, and you will be expected to contribute to that direction. Responsibilities - Design, build, and own data pipelines in our unified framework using Airflow, dbt, and Snowflake. - Implement the canonical data model in production, working within the architecture set by the Principal Data Engineer and contributing to it based on what implementation reveals. - Build automated validation and data quality checks into pipelines at defined stages, so that defects are caught before they reach consumers. - Instrument pipelines for observability, including freshness, lineage, and failure alerting. - Contribute to self-service capability that allows consuming teams to declare the data they need and receive it as governed output rather than a custom build. - Partner directly with consuming teams across invoicing, analytics, and reporting to understand their requirements and translate them into data they can rely on. - Migrate and consolidate existing data workloads onto the unified framework without disrupting the consumers depending on them. - Participate in an on-call rotation for pipeline and data quality incidents, and in the incident reviews that make fixes permanent. - Hold a high engineering bar: version control, testing, code review, CI/CD, and documentation that stays current. - Mentor engineers, share knowledge deliberately, and raise the technical level of the people around you. Qualifications - Bachelor's degree in Software Engineering, Computer Science, or a related field. - 5+ years of experience in data engineering, building and owning production data pipelines. - Strong Snowflake experience, including streams and tasks, warehouse sizing and performance tuning, SQL optimization, and working knowledge of RBAC, clustering, and micro-partitions. - Production experience with dbt and Airflow, including how to structure models and DAGs so that someone else can maintain them. - Strong programming expertise in Python and deep SQL proficiency. - Solid data modeling skills, with experience designing schemas that serve more than one consumer and the judgment to know when a requirement belongs in the shared model versus a consumer-specific extension. - Experience building testing and data quality validation into pipelines rather than checking data after the fact. - Disciplined engineering practices: version control, code review, automated testing, and CI/CD applied as a matter of course. - Ability to work directly with non-engineering consumers of data, understand what they actually need, and communicate constraints and tradeoffs clearly. Preferred - Experience with multi-tenant data platforms, including tenant isolation and handling per-customer variance within a shared model. - Experience modeling data that changes over time: event history, corrections and restatements, and the difference between point-in-time and as-of reporting. - Experience in healthcare claims, healthcare payments, or another regulated domain with PHI or PII handling and auditability requirements. - Familiarity with Datadog or comparable observability tooling applied to data pipelines. - Experience building internal tooling or platforms that other teams use directly. ***The US base salary range for this full-time position is: $125,241.00 - $187,862.00 The specific salary offered to a candidate may be influenced by a variety of factors including but not limited to the candidate's relevant experience, education, and work location. Please note that the compensation details listed in US role postings reflect the base salary only, and does not reflect the value of the total rewards compensation. ***