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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting and maintaining production-grade data platforms, with a strong focus on ETL/ELT pipelines and data quality. Proficient in collaborating with cross-functional teams to deliver data solutions that support machine learning and analytics.
Highest-signal resume keywords
Data EngineeringPythonSQLETL/ELT PatternsCloud Platforms
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Platform EngineeringData ArchitectureData ModelingWorkflow OrchestrationSystem DesignData QualityData LineageTestingCI/CDVersion Control
Soft Skills
Independent WorkOwnershipCollaboration
Tools & Technologies
AWSGCPAzureSparkDatabricksSnowflakeBigQueryApache AirflowDagsterPrefect
Industry Keywords
Life SciencesClinical TrialsGenomicsDrug DiscoveryData Governance
Tech Stack
Tools & technologiesAirflowApacheAWSAzureBigQueryCloudETLGoogle Cloud PlatformPythonSparkSQL
About the role
Key responsibilities & impact- 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 supporting 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
Requirements
What you’ll need- 5+ years of experience in data engineering, data platform engineering, or data-intensive software engineering
- Advanced professional experience with Python and SQL
- Strong experience designing and owning production-grade distributed data architectures
- Solid understanding of ETL/ELT patterns, data modeling, orchestration, and data observability
- Strong software engineering fundamentals, including testing, CI/CD, version control, and system design
- Experience handling large, complex, and heterogeneous datasets in cloud environments
- Ability to work independently, manage technical ambiguity, and take ownership of deliverables
- Experience with cloud platforms (AWS, GCP, or Azure) and technologies like Spark, Databricks, Snowflake, or BigQuery (bonus points)
- Experience with workflow orchestrators such as Apache Airflow, Dagster, or Prefect (bonus points)
- Domain experience in life sciences, clinical trials, genomics, or drug discovery datasets (bonus points)
- Exposure to data governance, lineage, or operating within regulated data environments (bonus points)
Benefits
Comp & perks- Competitive salary with travel expenses covered when travel is required
- Flexible work arrangements (Hybrid in Indianapolis, IN, or Fully Remote within the U.S. East Coast with occasional travel)
- Dynamic career growth with innovative, high-impact enterprise projects
- Long-term independent contractor agreement