Senior Data Engineer
- Evaluate and build proofs of concept for Cloud PaaS and IaaS offerings for data liquidity, data management and storage, data pipelines built on both traditional ETL as well as streaming platforms, master data management, data stewardship, record-linkage, NLP services, and more.
- 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
- Provide subject matter expertise on performance tuning and query optimization to full-stack peers, data analysts, and EDW developers
- Participate in building and owning a DevOps culture
- Continuously document your code, framework standards, and team processes
REQUIRED RELEVANT EXPRIENCE
- 5+ years of experience in an enterprise or commercial software development environment
- Extensive experience developing data-intensive solutions against an RDBMS, such as SQL Server, Postgres or Oracle.
- Highly skilled writing SQL queries, DML and DDL, CDC/change tracking patterns, indexes and performance tuning.
- Proficiency in using OOTB components, as well as implementing custom components or frameworks, on at least one traditional ETL platform, preferably SSIS, Informatica, or Talend
- 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
Ideal candidates come to the table with one or more additional competencies, such as:
- Healthcare data background
- 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