Senior Data Engineer

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Stellus Rx

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2022

🏥 Healthcare

💊 Pharmaceuticals

🤝 B2B

Healthcare • Pharmaceuticals • B2B

Stellus Rx is a pharmacist-led clinical pharmacy and digital pharmacy services company that delivers medication access, education, adherence and dispensing support for patients, providers, risk-bearing entities and employer groups. The company emphasizes tailored, pharmacist-driven care (personal pharmacists, condition-specific education, coordinated physician communication), home delivery and convenience packaging, plus white-labeled adherence solutions (Stellus Adhere, Stellus Engage) that integrate with clinical workflows and payer data to drive Medicare Advantage star performance and reduced total cost of care. Stellus Rx holds URAC Pharmacy Services and NBAP Digital Pharmacy accreditations and positions itself as a partner to value-based care platforms and employers to improve outcomes, experience and costs.

📋 Description

• Develop, construct, and maintain large-scale data processing systems that collect data from a variety of structured and unstructured sources — using AI code generation tools to accelerate pipeline authoring, reduce boilerplate, and improve code quality. • Build and optimize ELT pipelines using AI-assisted tooling to identify bottlenecks, suggest optimizations, and automate routine pipeline maintenance tasks. • Identify, design, and implement internal process improvements: use AI to automate manual processes, optimize data delivery, and re-design infrastructure for greater scalability — replacing manual analysis with AI-driven discovery of improvement opportunities. • Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources; use AI to accelerate infrastructure-as-code authoring and configuration. • Prepare data for data scientist exploration and discovery using AI-assisted data profiling and quality assessment tools — surfacing anomalies, schema drift, and data gaps faster than manual inspection allows. • Perform data wrangling and munging for downstream analytics and machine learning; leverage AI tools to generate and validate transformation logic against business rules. • Assemble large, complex datasets that meet functional and non-functional business requirements; use AI to rapidly evaluate dimensional modeling approaches and ontology alignment strategies. • Enable large-scale machine learning by designing and maintaining annotated datasets, elastic search approaches, and scalable data lake structures that support AI/ML workloads. • Create and maintain analytics pipelines that generate data and insight to power business decision-making; use AI-assisted analysis to proactively surface trends, anomalies, and opportunities within pipeline outputs. • Collaborate with data scientists, analysts, and business stakeholders on requirements for dimensional modeling, distributed ETL pipelines, and cross-repository data migration. • Evaluate, compare, and improve design patterns, data lifecycle approaches, and data ontology alignment — using AI to model trade-offs and accelerate proof-of-concept validation. • Work with data and analytics experts to continuously improve the functionality, reliability, and intelligence of data systems. • Perform root cause analysis on internal and external data and processes using AI-assisted investigation tools — replacing slow, manual log and lineage review with faster, AI-accelerated diagnostics. • Develop and maintain data quality frameworks; use AI to automate anomaly detection, schema validation, and data contract enforcement across pipelines. • Develop a strong understanding of company domains, strategic direction, and user needs to ensure data systems are aligned to business outcomes, not just technical requirements.

🎯 Requirements

• 4+ years of experience in a Data Engineer role. • Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field. • Advanced SQL knowledge and experience with relational databases and query authoring. • Demonstrated, hands-on experience using AI tools to accelerate data engineering tasks — pipeline development, data quality automation, code generation, or root cause analysis — with specific examples you can speak to. • Experience building and optimizing data pipelines, architectures, and datasets. • Strong analytic skills working with unstructured and disconnected datasets. • Experience with big data tools: Hadoop, Spark, Kafka, etc. • Experience with relational and NoSQL databases including Postgres and Cassandra. • Experience with pipeline and workflow management tools: Airflow, Luigi, Azkaban, or similar. • Experience with AWS cloud services: EC2, EMR, RDS, Redshift. • Experience with stream-processing systems: Storm, Spark Streaming, or similar. • Working knowledge of message queuing, stream processing, and highly scalable data stores. • Proficiency in object-oriented/scripting languages: Python, Java, Scala, C++, or similar. • Experience supporting cross-functional teams in dynamic, agile environments. • Familiarity with AI-assisted data quality or observability platforms (e.g., Monte Carlo, Soda, or similar). • Experience with LLM-based data processing pipelines or retrieval-augmented generation (RAG) architectures. • Healthcare data experience; familiarity with FHIR/HL7 standards a plus.

🏖️ Benefits

• Health insurance • Professional development opportunities