Senior Data Engineer (Data + Applied AI)

About Meridian Softworks : Meridian Softworks is a passion-fueled, mission-driven company that is trans-founded with a vision to transform healthcare for every trans life. We hope to make gender-affirming hormone therapy easily accessible at the touch of a button in every state of the US. This work is deeply personal and heart-driven, and we want teammates who care about the mission and the people we serve. For the right candidates, we present a rare opportunity to do well by doing good. Meridian Softworks offers an affirming, trans-centered, culturally inclusive, and fun work environment filled with purpose. About the Role The Senior Data and AI Engineer is a high-impact individual contributor on the Data and AI team. This role will be tasked with building, maintaining, and optimizing the data pipelines, transformation models, and BI deliverables. Applied AI (RAG pipelines, MLOps) is a growing area of the role, not the primary focus today. The right person for the role will be a skilled, self-sufficient engineer who takes well-defined architectural direction and executes it with precision, quality, and ownership. They will also contribute their own technical judgment to day-to-day implementation decisions. They are deeply hands-on: writing production dbt models, building Airflow DAGs, delivering clinical dashboards, and contributing to RAG pipelines and MLOps workflows within a regulated healthcare environment. You will also work closely with offshore contractors, reviewing their work, providing feedback, and ensuring output meets the team's quality and engineering standards. The ideal candidate will have a strong command of cloud data warehousing, dimensional modeling, Python, and applied AI tooling, as well as a collaborative mindset that thrives in a team-oriented environment. This role is an excellent opportunity for a senior engineer looking to deepen their full-stack data and AI expertise alongside experienced technical leadership. Responsibilites: Building and maintaining production-grade data pipelines in cloud data warehouses such as Google BigQuery or equivalent, following architectural standards set by the Director of Data and AI. Designing and developing dbt models across bronze, silver, and gold layers, including a focus on quality and governance via automated tests, documentation, and incremental load strategies. Creating and optimizing Airflow DAGs for data workflow orchestration, including scheduling, dependency management, error handling, and alerting. Implement dimensional data models and data mart structures — guided by the team's modeling standards — that support clinical BI and ML feature consumption. Crafting easy-to-understand visualizations and dashboards that align with commonly used business analytic standards in Looker or equivalent BI tools in close collaboration with product analytics, finance, operations, growth, and clinical stakeholders. Integrating healthcare data from sources such as EHRs, Stripe, 3rd-party APIs, and application database feeds, normalizing incoming data into the unified data platform. Applying HIPAA-compliant data handling practices, including PHI/PII masking, tokenization, audit logging, and role-based access controls across all pipeline and AI system work. Architecting and implementing RAG pipelines — including document ingestion, chunking, embedding generation, and retrieval — using frameworks such as LangChain or LangGraph Supporting MLOps workflows, including model training pipeline maintenance, deployment support, performance monitoring, and retraining triggers Code reviewing PRs from teammates, providing constructive technical feedback to peers, and upholding the team's engineering standards. Collaborating closely with product managers to understand requirements and deliver reliable data and AI products. Monitoring and triaging assigned pipeline and data quality failures, escalating architectural issues as appropriate. Documenting pipeline designs, data models, and technical decisions in alignment with the team's governance and lineage tracking standards. Evaluating new tools and frameworks, providing hands-on prototyping and technical assessments. Must-Have Requirements: 5+ years of hands-on experience in data engineering, analytics engineering, or a closely related role. 2+ years of experience working within the healthcare industry, including working knowledge of healthcare data standards, clinical workflows, regulated data environments, and domain-specific data visualizations. Working knowledge of HIPAA — including PHI/PII classification, data masking, audit logging, and access control requirements. Proven production experience with at least one major cloud data warehouse: BigQuery, Snowflake, or Redshift — including advanced SQL and query optimization. Strong hands-on experience with dbt (Core or Cloud), including incremental models, tests, documentation, and multi-environment workflows. Deep experience with Apache Airflow for workflow orchestration, including DAG design, scheduling,