Lead Software Engineer
- Evaluate and build proofs of concept for Cloud PaaS and IaaS offerings that promote data liquidity and support support data management, infrastructure, AI services, and industry interoperoperability.
- Write traditional code and server-less functions using the right language for the task, which may be SQL, Python, C#, Java, PowerShell, SSIS/BIML, and others.
- Participate in build-buy-open source decisions for parsing and managing industry standard formats such as FHIR/NDJSON, pipe-and-hat HL7, and x12 EDI.
- Evaluate, select, and apply Cloud and OO design and resiliency patterns.
- Build APIs and data microservices to share our data with internal and external partners, and write interfaces to public data sets to enrich our analytics data stores.
- Participate in building and owning a DevOps culture.
- Continuously document your code, framework standards, and team processes.
- Other duties and responsibilities as assigned.
EDUCATION, TRAINING, AND PROFESSIONAL EXPERIENCE
- 5+ years of experience in an enterprise or commercial software development environment
- 2+ years leading a team of engineers
- Extensive experience developing data-intensive solutions in a Cloud environment.
- Enterprise experience developing solutions that use event sourcing and/or Big Data architectures.
- Team player who is not afraid to ask questions, take risks, share in owning team victories as well as team failures
- Good communicator – both written and verbal – with high emotional intelligence
- Ability to focus on MVP and shipping software while remaining cognizant of the long-term costs of technical debt
- Healthcare data background a must
- MUST HAVE THE RIGHT TO WORK IN THE US WITHOUT VISA SPONSORSHIP
Ideal candidates come to the table with one or more additional competencies, such as:
- Exposure to Enterprise Data Warehouse , Data Lake, Big Data, unstructured data, in-memory data stores
- Familiarity with NoSQL database systems such as MongoDB, Cassandra, CosmosDB, neo4j etc.
- Familiarity with Kimball-like star and snowflake data models and columnstore Indexing
- Experience building metadata-driven data pipeline frameworks for quickly mapping, onboarding, and ingesting data from a wide variety of partner sources
- Enterprise experience with data movement and management in the Cloud utilizing some combination of Azure and/or AWS features such as Data Factory, Blob Storage, Service Bus, Kafka, Redis, S3 Buckets, Azure Automation, Machine Learning, elastic search, Glue etc.
- Data Science training or experience to better understand and collaborate with one of our key data consumers (notably, this is still an engineering role and not a data science role)
- CRM experience, such as MS Dynamics or SalesForce