Senior Data Engineer
New
W
WorkanaLife sciences
Fully Remote within the U.S. East Coast with occasional travelContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in data engineering, data platform engineering, or data-intensive software engineering.
- Required Skills
- PythonSQLCloud ComputingData engineeringCI/CDData modeling
Requirements
- Have 5+ years of experience in data engineering, data platform engineering, or data-intensive software engineering.
- Bring advanced professional experience with Python and SQL.
- Have strong experience designing and owning production-grade distributed data architectures.
- Understand ETL/ELT patterns, data modeling, orchestration, and data observability.
- Have strong software engineering fundamentals, including testing, CI/CD, version control, and system design.
- Have experience handling large, complex, heterogeneous datasets in cloud environments.
- Be able to work independently, manage technical ambiguity, and take ownership of deliverables.
- Cloud platform experience with AWS, GCP, or Azure and technologies such as Spark, Databricks, Snowflake, or BigQuery is a bonus.
- Experience with workflow orchestrators such as Apache Airflow, Dagster, or Prefect is a bonus.
- Domain experience in life sciences, clinical trials, genomics, or drug discovery datasets is a bonus.
- Exposure to data governance, lineage, or regulated data environments is a bonus.
Responsibilities
- Architect, build, and maintain production-grade data platforms and scalable ELT/ETL pipelines.
- Ingest, transform, and model complex structured and unstructured scientific and clinical datasets.
- Define data architecture patterns, engineering standards, and best practices across the team.
- Collaborate with scientists, ML engineers, and business stakeholders to turn domain needs into data solutions.
- Design data infrastructure that supports machine learning training, inference, and analytics workloads.
- Ensure data quality, lineage, reproducibility, security, and system observability.
- Optimize pipeline performance, architectural bottlenecks, and infrastructure cost efficiency.
- Participate in technical design discussions, code reviews, and architectural decision-making.
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