Key skills

PythonSQLPySpark, Ray, and DaskAWS, GCP, and AzureSnowflake, BigQuery, Databricks, and RedshiftAirflow, Dagster, and PrefectdbtParquet, ORC, and AvroGreat Expectations and Monte CarloVCF, BAM/SAM, FASTQ, FHIR, and DICOMKafka, Kinesis, and FlinkProduction machine learning and LLM-based RAG pipelinesAIMachine LearningMLLLMRAGETLELTData EngineeringSnowflakeBigQueryRedshiftDatabricksSparkKafkaAirflowAWSAzureGCPKinesisCI/CD

About this role

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

Requirements:

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