Note: The job is a remote job and is open to candidates in USA. Alden Scientific is seeking a Principal Data Engineer to lead the architecture and development of its core data infrastructure. The role builds scalable data platforms and pipelines for multi-omic, clinical, and longitudinal health data, while setting technical standards and supporting production AI services.
Responsibilities
- Architect and maintain a multi-tenant, cloud-native data lakehouse that supports multi-omic (genomics, metabolomics, proteomics), biomarker, and wearable streaming data
- Design scalable data models and storage strategies optimized for both real-time analytical queries and large-scale batch ML model training
- Establish data governance, security, and privacy standards compliant with HIPAA and relevant healthcare data protection regulations
- Build robust, fault-tolerant ETL/ELT pipelines using modern orchestrators (e.g., Airflow, Dagster, or Prefect) and processing engines (e.g., Spark, Snowflake, BigQuery)
- Collaborate with ML engineers to implement feature stores, dataset versioning, and automated data validation pipelines that support production AI services
- Optimize pipeline performance, storage costs, and query latency across heterogeneous scientific datasets
- Set technical standards, best practices, and architectural roadmaps for data engineering across the organization
- Mentor and guide data engineers, advocating for software engineering rigor (testing, CI/CD, data quality monitoring)
- Work closely with product, science, and software engineering teams to turn biological and clinical requirements into technical implementations
Skills
- 8+ years of experience in data engineering, software engineering, or data architecture
- Expert proficiency with Python, SQL, and distributed computing frameworks (e.g., PySpark, Ray, or Dask)
- Proven experience designing enterprise-scale data platforms on AWS, GCP, or Azure using technologies like Snowflake, BigQuery, Databricks, or Redshift
- Strong hands-on experience with modern data orchestration tools (e.g., Airflow, Dagster) and data modeling frameworks (e.g., dbt)
- Solid foundation in database internals, data serialization formats (Parquet, ORC, Avro), and schema design
- Experience implementing data quality, observability, and testing frameworks (e.g., Great Expectations, Monte Carlo)
- Experience with biological, genomic, or healthcare data formats (VCF, BAM/SAM, FASTQ, FHIR, DICOM)
- Prior work supporting production machine learning, vector databases, or LLM-based RAG pipelines
- Familiarity with streaming data platforms (Kafka, Kinesis, Flink)
- Background in biopharma, digital health, or longevity research
Benefits
- Equity options
- Comprehensive health benefits
- Flexible PTO
- Professional development support
- Exceptional US-based candidates with prior success in remote roles will be considered; remote team members should expect periodic travel to Boston for collaborative work.
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