Note: The job is a remote job and is open to candidates in USA. Releady is partnering with a leading healthcare technology company to hire a Data Engineer, Principal for its Data Services team. This role involves designing and implementing cloud data lakes and data warehouse solutions while mentoring engineering talent and influencing enterprise architecture decisions.
Responsibilities
- Lead the design, development, and implementation of scalable data pipelines supporting enterprise data lakes, data warehouses, and data marts
- Engineer robust ELT/ETL solutions that ingest, process, and curate structured and semi-structured data from diverse internal and external sources
- Apply advanced data modeling techniques, including Data Vault 2.0, dimensional, and domain-oriented models, to support analytics and data products
- Partner with Solution Design, Architecture, and Product teams to ensure technical designs are implemented accurately, efficiently, and securely
- Build and optimize data solutions on cloud platforms such as Snowflake, Databricks, and Synapse, with a focus on performance, scalability, reliability, and cost efficiency
- Implement data quality, validation, observability, lineage, and governance controls embedded directly into data pipelines
- Champion and apply DevOps and DataOps best practices, including CI/CD, automated testing, infrastructure as code, monitoring, and alerting
- Identify performance bottlenecks, reliability risks, and optimization opportunities across data platforms and workflows
- Provide hands-on technical leadership and mentorship to senior and mid-level data engineers, promoting engineering standards and best practices
- Collaborate using agile methodologies to plan work, refine technical stories, and deliver iteratively with predictable outcomes
- Support integration of AI/ML-ready data assets, ensuring data is trustworthy, well-modeled, and accessible for advanced analytics use cases
- Act as a technical thought leader, advocating for modern data engineering patterns, tools, and practices aligned to enterprise strategy
Skills
- Bachelor's degree or equivalent experience, with a minimum of ten years of relevant data engineering experience
- Experience supporting enterprise AI/ML, advanced analytics, and data product ecosystems
- Expertise in Data Vault 2.0, dimensional modeling, Lakehouse architecture, or domain-driven data design
- Demonstrated ability to influence enterprise architecture decisions and mentor senior engineering talent
- Expert-level understanding of data management practices, including data modeling (Data Vault 2.0), master data management, data integration, architecture, virtualization, warehousing, privacy, and security
- Demonstrated enthusiasm for AI and emerging technologies, with hands-on experience applying AI-driven solutions in enterprise environments
- Hands-on experience with SQL, Python, DBT Cloud, and DBT Core; expert-level fluency in NoSQL and database platforms such as Snowflake, Synapse, or Databricks
- Proven ability to design and scale large-volume, high-performance data platforms leveraging parallel and distributed architectures
- Advanced knowledge of CI/CD, source control, and agile delivery tooling such as Git, Bitbucket, and Jira
- Experience in healthcare, regulated environments, or large enterprises is strongly preferred
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