Senior Data Engineer
Onapsis
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Compensation
Salary & market context
Salary not listed
- Databricks, Snowflake, Synapse experience is a bonus.
Requirements
Top requirements
- s:
- 5+ years of proven experience as a Data Engineer or in a similar role with a deep understanding of data architecture and cloud-based ETL/ELT frameworks.
- Strong experience with AWS (preferably) or Azure , particularly with Glue, EMR, S3, Lambda. Databricks, Snowflake, Synapse experience is a bonus.
- Proficiency in big data technologies such as Apache Spark, Kafka, Hadoop, and Databricks for distributed data processing.
Perks & setup
Benefits candidates care about
- A role in shaping the future of protecting the most critical applications that run the world's business and a career that grows as the company grows.
- A unique culture of high achievement and teamwork.
- Supportive and humble colleagues are the space's top problem solvers and innovators.
- Financial security through competitive compensation and incentives.
Why candidates care
Benefits & perks
- A role in shaping the future of protecting the most critical applications that run the world's business and a career that grows as the company grows.
- A unique culture of high achievement and teamwork.
- Supportive and humble colleagues are the space's top problem solvers and innovators.
- Financial security through competitive compensation and incentives.
- The location for this role is Heidelberg. This is a hybrid role, so candidates must be commutable to Heidelberg.
- About Onapsis:
- Onapsis protects the business applications that run the global economy. The Onapsis Platform delivers vulnerability management, change assurance, and continuous compliance for business applications from leading vendors such as SAP, Oracle, and others. The Onapsis Platform is powered by the Onapsis Research Labs, the team responsible for the discovery and mitigation of more than 1,000 zero-day vulnerabilities in business applications.
- Onapsis is headquartered in Boston, MA, with egional offices in Heidelberg, Germany; Buenos Aires, Argentina; Texas, USA, and now in Bucharest, Romania; and proudly serves hundreds of the world's leading brands, including close to 30% of the Forbes Global 100, six of the top 10 automotive companies, five of the top 10 chemical companies, four of the top 10 technology companies, and three of the top 10 oil and gas companies.
Start here
Requirements
- s:
- 5+ years of proven experience as a Data Engineer or in a similar role with a deep understanding of data architecture and cloud-based ETL/ELT frameworks.
- Strong experience with AWS (preferably) or Azure , particularly with Glue, EMR, S3, Lambda. Databricks, Snowflake, Synapse experience is a bonus.
- Proficiency in big data technologies such as Apache Spark, Kafka, Hadoop, and Databricks for distributed data processing.
- Proficiency with Python libraries for data processing and ML (e.g., Pandas, NumPy, Polars, Scikit-learn, PyTorch, TensorFlow).
- Hands-on experience in building real-time data processing and AI/ML-driven analytics solutions (SageMaker, Bedrock, NLP, Power BI).
- Ability to architect and manage data lakehouse solutions (Iceberg / Delta Lake / Hudi) or classic warehouse solutions (Redshift, Snowflake).
- Familiarity with compliance and audit requirements (SOX, SOC 1/2, GDPR) and implementing data governance and security frameworks.
Responsibilities
What you'll do
- s:
- Architect and Design Scalable Data Solutions: Design/develop/maintain Data lakehouse solutions (Iceberg/Delta Lake /Hudi) applying industry best practices and structuring / optimizing the data according to data access patterns.
- Data Pipeline Development: Implement ETL/ELT pipelines using cloud technologies (Spark / pySpark / Glue, Kinesis Streams / Iceberg) to load the data into a Lakehouse for both efficient ML processing and UI reporting.
- Implement data models and data processing frameworks (Spark, Kafka, Snowflake) to ingest, transform, and load large datasets into Data Lakehouse techs (Apache Iceberg, Apache Delta Lake or Apache Hudi), ensuring high availability and reliability of data.
- Advanced Data Integration: Develop solutions that integrate multiple data sources into Snowflake or similar data warehouses to enable real-time analytics and reporting across dashboards.
- AI/ML Integration: Collaborate with cross-functional teams to co-develop AI-driven features identifying patterns and anomalies in client data using AI/ML technologies (python).
- Compliance and Security: Ensure compliance with industry standards and secure best practices (SOX, SOC 1/2), by implementing data governance frameworks, monitoring data pipelines, and optimizing cloud database architectures to protect sensitive information.
- Stakeholder Collaboration: Work closely with stakeholders, including analysts, engineers, and product managers, to understand their data needs, propose solutions, and drive data-driven decision-making by delivering actionable insights.
Role snapshot
About the role
About the job
The world's most critical--and at risk--business applications have been neglected for far too long. Onapsis eliminates this blind spot by providing cybersecurity solutions dedicated to business-critical applications. Whether running on premises, in the cloud, or in a hybrid environment, Onapsis helps nearly 30% of the Forbes Global 100 understand the threats and risks across their SAP and Oracle landscapes.
We are seeking a Senior Data Engineer to join our mission-driven team. This role is ideal for experienced data engineers with a proven track record in architecting scalable data pipelines, leveraging cloud technologies, and contributing to high-impact cybersecurity solutions. You will be responsible for building high-performance ETL frameworks, optimizing data platforms, and contributing directly to the enhancement of our customers' threat detection, response, and remediation capabilities.
What you will be doing, your legacy:
More detail
Nice to have
- s:
- Experience with advanced data architecture principles (medallion architecture, materialized views, task scheduling).
- Experience using BI tools (e.g., Power BI, Tableau) for real-time analytics and operational reporting
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