Data Engineer (pharma; 100% remote)

You will be the backbone of our data platform — designing, building and maintaining the pipelines, transformations and APIs that power Finance reporting, analytics and operational tooling across the organization. You will work in a modern cloud-first stack centered around Snowflake, collaborating with data analysts, UX designers and software engineers to deliver reliable, scalable and well-governed data solutions. This role requires both deep technical expertise and the ability to translate complex Finance data requirements into clean, production-grade engineering.

• 5+ years of experience in data engineering, with at least 2 years working extensively with Snowflake in production.
• Strong hands-on experience with ETL design, data blending, and complex data transformation logic.
• Proficiency in advanced SQL and Python for data engineering.
• Experience building and maintaining data pipelines with orchestration tools such as Airflow, dbt, or similar.
• Solid understanding of cloud data warehouse concepts: partitioning, clustering, materialized views, query optimization, and cost governance.
• Experience working with Finance data (general ledger, cost center structures, P&L, FP&A, or similar).
• Proficiency with GitHub for version control, branching, and code review.
• Experience developing and documenting backend RESTful APIs.
• Familiarity with CI/CD pipelines for data workloads.

Nice to have:

• Experience with SAP (S/4HANA, BW4) or other ERP source systems.
• Knowledge of data governance frameworks and metadata management tools (e.g. Collibra, DataHub).
• Exposure to Snowflake's data sharing and Marketplace capabilities.
• Familiarity with dbt for transformation and testing.
• Understanding of dimensional modeling.
• Experience in regulated industries such as pharma, finance, and life sciences.

• Data Engineering & Architecture: Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and deliver Finance data from multiple source systems into Snowflake.
• Implement data blending strategies consolidating data from ERP systems, flat files, APIs, and databases into unified datasets.
• Apply Snowflake-native features: dynamic tables, streams, tasks, materialized views, data sharing, and time travel.
• Model data using dimensional and modern approaches (e.g. Data Vault, medallion architecture) for Finance use cases.
• Ensure data quality, consistency, and lineage via robust testing and monitoring.
• Optimize query performance and cost efficiency within Snowflake.
• Pipelines & Orchestration: Build and maintain data pipelines using orchestration tools such as Apache Airflow, dbt, or equivalents.
• Implement CI/CD practices for data pipelines using GitHub Actions or similar.
• Monitor pipeline health, set alerting, and manage data failure incidents.
• Backend & API Development: Design and develop RESTful APIs to expose Finance data.
• Collaborate with frontend and full-stack developers to define data contracts ensuring performance and reliability.
• Implement authentication, authorization, and data access governance for sensitive Finance data.
• Cloud & Infrastructure: Work in AWS cloud environments applying best practices on security, scalability, and cost.
• Contribute to infrastructure-as-code with tools like Terraform or equivalents.
• Champion data security and compliance regarding financial data sensitivity and regulatory requirements.
• Collaboration & Standards: Partner with Finance domain experts and analysts to translate data needs into technical solutions.
• Contribute to and enforce data engineering standards, documentation, and best practices.
• Mentor junior engineers and act as a technical reference within the domain.