Opportunity

In this role, you will strengthen our data platform by architecting and optimizing pipelines, implementing automation, and improving data observability and quality. You will help establish AI‑assisted development workflows, improve engineering productivity, and enable the team to better leverage data for customer and internal use cases. Your work will directly empower other engineers by improving tooling, processes, and data reliability.

Culture

• Highly collaborative and supportive team environment where engineers help each other grow.

• Success comes from curiosity, learning quickly, adapting to new challenges, and delivering value through partnership.

• Team members thrive when they approach problems with ownership, pragmatism, and a willingness to solve ambiguous challenges.

Key Responsibilities

• End‑to‑end data ownership: Partner with Product, Data, and Engineering teams to lead complex data initiatives from design through deployment while ensuring security, compliance, and reliability across data pipelines and workflows.

• Drive data‑driven experiences: Transform complex clinical and operational datasets into intuitive, high‑quality, and discoverable data assets that support internal stakeholders and downstream product experiences.

• Shape engineering culture: Champion engineering best practices, contribute to architectural decision‑making, and support knowledge‑sharing across cross‑disciplinary teams.

• AI‑leveraged Engineering: Use LLMs to accelerate tasks such as documentation, code generation, data modeling, test/synthetic data creation, and workflow automation.

Required Qualifications

• 3 years of experience in data engineering or related roles.

• Strong proficiency in SQL and at least one of Python, Scala, or Java.

• Experience with distributed data processing frameworks such as Apache Spark and platforms like Databricks.

• Experience with AWS services (S3, Lambda, EMR, DynamoDB, CloudWatch, or equivalent).

• Working knowledge of Terraform or other infrastructure‑as‑code frameworks.

• Solid understanding of database concepts including relational and NoSQL (e.g., MongoDB).

• Experience working with Kafka or other event‑driven systems.

• Familiarity with CI/CD tools such as GitLab CI, GitHub Actions, or similar.

• Ability to work with minimal supervision, driving projects from requirements to delivery.

• Strong problem‑solving, communication, and documentation skills.

Preferred Qualifications

• Experience with medical, clinical, or regulated data (HIPAA, HITRUST, etc.).

• Background in data analytics, data modeling, or data product development.

• Experience working across data and application teams building end‑to‑end data‑driven features.

Technical Skills

• Proficiency in development languages such as Python/Scala/Java, SQL, Spark or similar coding or scripting languages.

• Experience with, Databricks, AWS, Terraform, GitLab/GitHub, MongoDB, Kafka, CI/CD and similar technologies.

• File management skills and logical problem solving.

• Ability to work with data models and structured/unstructured data.

• Knowledge of industry engineering practices (e.g., code coverage, naming conventions, encapsulation).

• Familiarity with Agile methodologies.

• Strong understanding of data manipulation and transformation techniques.

• Ability and desire to learn new tools, processes, and technologies.

• Attention to detail and strong written/verbal communication skills.

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