Be Part Of A High-Performing Team:Join a mission-driven technology organization focused on improving the aging and long-term care experience through modern data, analytics, machine learning, and digital product innovation. This team is building scalable solutions that help individuals, families, and caregivers navigate a historically fragmented care ecosystem with greater clarity, dignity, and support. The environment is collaborative, purpose-driven, and highly cross-functional, bringing together data engineers, analysts, data scientists, ML/AI engineers, and product teams to create reliable data foundations that power meaningful business and customer outcomes.
What's In Store For You:This is a full-time opportunity available to remote applicants residing in Eastern Standard Time states. The role offers the chance to work on modern data architecture, Lakehouse engineering, machine learning enablement, and production-grade analytics infrastructure. The organization provides a strong employee support model, including healthcare coverage, retirement programs, paid time off, paid family leave, tuition reimbursement, student loan repayment, training and certification support, wellness resources, caregiver support, and mental health services.
How You Will Make An Impact - Design, build, and maintain scalable ETL/ELT pipelines that support analytics, machine learning, and operational data needs.
- Develop reliable ingestion frameworks for batch and streaming data sources using Python, SQL, Spark, and Databricks.
- Build and maintain data models across raw, curated, semantic, and domain-specific layers using Delta Lake.
- Create dimensional models, star schemas, and curated datasets that enable business KPI tracking, reporting, and ML use cases.
- Improve pipeline quality through automated testing, data validation, anomaly detection, documentation, monitoring, and alerting.
- Partner with analysts, data scientists, ML engineers, product teams, and platform engineers to deliver reusable, high-quality data assets.
- Optimize Spark jobs, SQL queries, cluster configurations, and storage patterns for performance, reliability, and cost efficiency.
- Apply strong data governance, RBAC, privacy, PII handling, and Unity Catalog best practices across the data ecosystem.
- Help raise engineering standards by reducing technical debt, improving repeatability, and mentoring others on modern data engineering practices.
Do you bring proven success in modern data engineering and Lakehouse architecture? - 7+ years of experience in data engineering, analytics engineering, or related data-focused engineering roles.
- Strong hands-on experience with Python, SQL, Spark, and distributed data processing.
- Production experience with Databricks, Delta Lake, and Lakehouse architecture.
- Deep understanding of ETL/ELT design, data modeling, data quality, and scalable pipeline development.
- Experience building production-grade data pipelines with reliability, monitoring, SLAs, and operational support in mind.
- Ability to design curated datasets, dimensional models, semantic layers, and analytics-ready data products.
- Experience supporting analytics, machine learning, data science, and product stakeholders.
- Strong communication skills with the ability to clarify business/data requirements and explain technical decisions clearly.
- Comfort working in a collaborative, fast-moving, purpose-driven environment.
- Must reside in an eligible Eastern Standard Time state.
- This role is not eligible for employment visa sponsorship
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