Job Description:
- 6+ years of experience in Data Engineering.
- Strong data architecture and system design skills with the ability to make sound trade-offs around data modeling, partitioning, and scalability.
- Demonstrated end-to-end ownership of production data pipelines, including design, deployment, monitoring, and operational support.
- Expertise in Python and SQL as primary tools; experience with Hive/Hadoop is a plus.
- Strong focus on data quality, validation, reconciliation, anomaly detection, and audit ability.
- Experience working with finance, risk, compliance, or other highly regulated data domains requiring strong data lineage and governance.
- Proven experience designing and building transaction and fact tables.
- Advanced SQL skills, including window functions, CTEs, CASE statements, and working across MySQL/PostgreSQL environments.
- Strong Python expertise, including pandas, SQLAlchemy, type hints, data classes, and object-oriented pipeline development.
- Experience with workflow orchestration platforms such as Airflow or similar tools.
- Strong understanding of data modeling concepts including fact/dimension modeling, normalization, and medallion architecture.
- Experience with YAML-based configurations, CI/CD practices, and Git-based development workflows.
Requirements
Strongly Preferred:
- Experience with enterprise data platforms, internal data tooling ecosystems, metadata/catalog solutions, secrets management tools, developer productivity platforms, and ETL orchestration frameworks.
Nice-to-Have:
- Experience with Hive, Hadoop, Spark, or related big data technologies.
Benefits
· Competitive salary and performance-based bonuses.
· Comprehensive insurance plans.
· Collaborative and supportive work environment.
· Chance to learn and grow with a talented team.
· A positive and fun work environment.
Skills Required
- 6+ years of experience in data engineering
- Strong data architecture and system design skills, including data modeling, partitioning, scalability, and trade-off analysis
- End-to-end ownership of production data pipelines, including design, deployment, monitoring, and operational support
- Expertise in Python and SQL
- Experience with data quality, validation, reconciliation, anomaly detection, and auditability
- Experience with finance, risk, compliance, or other highly regulated data domains
- Experience designing and building transaction and fact tables
- Advanced SQL, including window functions, CTEs, CASE statements, and MySQL/PostgreSQL environments
- Strong Python expertise, including pandas, SQLAlchemy, type hints, data classes, and object-oriented pipeline development
- Experience with Airflow or similar workflow orchestration platforms
- Understanding of fact/dimension modeling, normalization, and medallion architecture
- Experience with YAML-based configurations, CI/CD practices, and Git-based development workflows
- Experience with enterprise data platforms, internal data tooling ecosystems, metadata/catalog solutions, secrets management tools, developer productivity platforms, and ETL orchestration frameworks
- Experience with Hive, Hadoop, Spark, or related big data technologies
What We Do
Prescience is a Danish SaaS company that provides collaborative supply-chain execution software for industrial businesses. Its platform helps customers and suppliers plan production, track manufacturing progress, monitor execution, manage capacity, improve quality assurance, collect as-built documentation, and coordinate inventory and inbound and outbound logistics. By integrating supplier data in real time, Prescience delivers visibility, risk detection, and operational control across dispersed global supply chains.
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