Sr. Data Engineer

Who Are We?

Prolaio believes that continuous learning and collaboration can make a significant difference in how heart care is administered. We are creating smarter ways to address heart disease and heart risks by uniting patients, care teams, and researchers on a secure, technology-enabled platform that drives clinical innovation and offers a path towards better patient outcomes.

This is precision cardiology, and we know it's within reach.

The Overview

As a Senior Data Engineer, you will design, build, and run that pipeline in a regulated environment. You will work closely with the data science/clinical teams, which owns the medical and research questions. You will also work with the Data Architect on platform standards, data models, and governance.

Build and Evolve the Data Platform (Prolaio Data Ecosystem)

• Design, build, and maintain batch, streaming, and hybrid pipelines orchestrated with Dagster. These cover ingestion, validation, transformation, enrichment, and publishing of the data.
• Develop and maintain dbt models in BigQuery, including staging, intermediate, and mart layers, with tests, documentation, and clear ownership.
• Ingest and normalize heterogeneous sources (both structured and unstructured data), including cardiology wearables and device signals, EHR, claims, medication, and patient-reported data.
• Make well-reasoned decisions about storage, compute, partitioning, cost, and APIs, and contribute to the target-state architecture and the reference patterns.

Data Quality, Lineage, and Compliance

• Contribute to data lifecycle controls into pipelines: retention, access control, lineage, provenance, reproducibility, and auditability.
• Use Dagster assets and dbt lineage to keep data traceable from source to output.
• Support validated operation in a regulated setting by documenting pipelines, keeping traceability, and working with Quality and Security teams.
• Back every pipeline change with thorough automated tests and data quality checks.

Enable Data Science and ML

• Treat data scientists as your customers, and give them the tools and processes to experiment and iterate efficiently.
• Contribute to the ML workflows that support feature generation, training, deployment, and monitoring.
• Contribute to internal applications and Python libraries

Operate and Monitor

• Own CI/CD for the data platform using GitLab CI, including dbt and Dagster deployments.
• Help monitoring pipeline health, performance, and BigQuery cost with Datadog, and take part in incident response.
• Help mentoring other engineers and setting engineering standards for the data team.

Who You Are?

• 6+ years in data engineering
• Expert Python and SQL
• Experience with GCP (or AWS/Azure)
• Production experience with dbt, including modeling, testing, macros, and incremental models.
• Experience with BigQuery or a comparable cloud data warehouse (Databricks, Redshift).
• Experience with Dagster or a comparable orchestrator (Airflow, Prefect).
• Experience with Docker and large-scale data processing.
• Experience with structured, semi-structured, and unstructured data, including time-series data.
• Understanding of MLOps concepts and production ML workflows.
• Experience collaborating with data scientists, product, and clinical or quality stakeholders.
• Experience with Docker and Kubernetes.

Additional Qualifications (Nice to Haves)

• Hands-on GCP experience (IAM, Cloud Storage, Cloud Run, Pub/Sub, networking).
• Infrastructure as code with Terraform
• Experience with Datadog
• Metadata, lineage, and catalog tools, plus retrieval or vector tooling for AI use cases.
• Experience with Databricks
• Experience working with healthcare, life sciences, medtech, or other patient data.
• Experience in regulated or validated environments (SaMD, FDA, GxP, HIPAA).
• Streaming tools such as Pub/Sub or Kafka.