Senior Data Engineer
Lyric is the trusted leader in healthcare decision intelligence. Built on more than 35 years of proven results, Lyric combines responsible AI with clinical, payment, regulatory, and policy expertise to deliver transparent, auditable insights in milliseconds across real-time claims workflows. Lyric augments human decision-making so health plans can make faster, more accurate payments while maintaining full control. Today, Lyric supports 200 million lives, with nine of the top 10 U.S. health plans relying on its platform to reduce waste, improve efficiency, and support accurate payments.
Applicants must already be legally authorized to work in the U.S. Visa sponsorship/sponsorship assumption and other immigration support are not available for this position.
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
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Design, build, and own data pipelines in our unified framework using Airflow, dbt, and Snowflake.
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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.
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Build automated validation and data quality checks into pipelines at defined stages, so that defects are caught before they reach consumers.
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Instrument pipelines for observability, including freshness, lineage, and failure alerting.
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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.
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Partner directly with consuming teams across invoicing, analytics, and reporting to understand their requirements and translate them into data they can rely on.
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Migrate and consolidate existing data workloads onto the unified framework without disrupting the consumers depending on them.
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Participate in an on-call rotation for pipeline and data quality incidents, and in the incident reviews that make fixes permanent.
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Hold a high engineering bar: version control, testing, code review, CI/CD, and documentation that stays current.
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Mentor engineers, share knowledge deliberately, and raise the technical level of the people around you.
Qualifications
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Bachelor's degree in Software Engineering, Computer Science, or a related field.
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5+ years of experience in data engineering, building and owning production data pipelines.
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Strong Snowflake experience, including streams and tasks, warehouse sizing and performance tuning, SQL optimization, and working knowledge of RBAC, clustering, and micro-partitions.
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Production experience with dbt and Airflow, including how to structure models and DAGs so that someone else can maintain them.
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Strong programming expertise in Python and deep SQL proficiency.
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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.
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Experience building testing and data quality validation into pipelines rather than checking data after the fact.
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Disciplined engineering practices: version control, code re