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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Data Engineering with a focus on building and maintaining production-grade data systems, data modeling, and implementing data governance. Proficient in Python, SQL, and modern data stack tools to deliver scalable and reliable data solutions.
Highest-signal resume keywords
Data EngineeringData ModelingPython ProficiencySQL ProficiencyAWS Experience
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 EngineeringData ModelingPythonSQLProduction-Grade Data SystemsData GovernanceMetadata ManagementInfrastructure-As-CodeRelational DatabasesData Quality Standards
Soft Skills
CollaborationMentoringCommunication
Tools & Technologies
DbtAirflowTerraformSnowflakeBigQueryRedshiftObject Storage
Industry Keywords
BiotechPharmaDrug DiscoveryAIDeeptech
Tech Stack
Tools & technologiesAirflowAmazon RedshiftAWSBigQueryPythonSQLTerraform
About the role
Key responsibilities & impact- Own AQEMIA's Bronze → Silver → Gold data pipelines end to end, from ingestion through transformation and delivery
- Model canonical scientific entities such as compounds, structures, assays and predictions
- Establish identity, provenance and trustworthy lineage across heterogeneous and messy sources
- Set and uphold data quality standards through monitoring, validation, testing and alerting
- Strengthen data governance and observability so datasets remain trusted and accessible
- Partner with ML engineers, data scientists and researchers to build curated, model-ready datasets
- Translate scientific and business requirements into scalable data solutions
- Drive data architecture and engineering best practices, including data modeling, testing, documentation, orchestration and deployment
- Collaborate with the Engineering Manager and Staff Data Engineer on roadmap execution
- Build self-service capabilities and future APIs for service-based integration and automation
- Conduct code reviews and mentor junior engineers
Requirements
What you’ll need- 7-10 years of experience in Data Engineering, ideally in fast-paced technology, scientific, AI or data-intensive environments
- Strong software and data engineering skills
- Deep experience in data modeling and relational databases
- Strong proficiency in Python and SQL
- Experience building and maintaining production-grade data systems
- Hands-on experience with dbt, Airflow, or similar modern data stack tooling
- Any STEM degree or equivalent experience
- Experience with AWS
- Experience with infrastructure-as-code (Terraform), modern data warehousing (e.g. Snowflake, BigQuery, Redshift), and object storage
- Experience in drug discovery, biotech, pharma or deeptech environments
- Exposure to AI-driven or data-intensive workflows, or experience working across biology, ML and chemistry
- Experience implementing data governance, lineage and metadata management solutions
- Track record of improving platform scalability, reliability and operational maturity
Benefits
Comp & perks- Hybrid work arrangement
- Opportunity to shape data platform architecture
- Mentorship and knowledge-sharing opportunities