Sr. Data Engineer

• Lead highly complex data engineering projects with extensive latitude for independent judgment.
• Own dataset management, including schemas, data dictionaries, source provenance, data lineage, and reproducible dataset versions, ensuring datasets remain current, accurate, and fit for purpose.
• Own all aspects of the Extract, Transform, Load (ETL) process, including reliable ingestion, retries, backfills, and recovery from partial failures.
• Identify and resolve data quality issues through automated schema validation, deduplication, and missing-data checks.
• Implement monitoring, testing, and CI/CD processes in collaboration with ML engineers to ensure high data quality and system reliability.
• Collaborate closely with data scientists to: Assemble high-quality datasets that meet product requirements. Perform data analysis and feature engineering. Develop pipelines that support ML development and evaluation.
• Engineer ontologies for various Knowledge Graph (KG) applications.
• Partner with product managers to define and expand taxonomies.
• Address data compliance and governance requirements, ensuring applicable regulatory standards are met.
• Produce reports and actionable insights using dashboards and analytical tools.
• Support the integration of AI/ML tools into security product and service workflows.

• At least 7 years of relevant experience in data engineering, software engineering, or related roles, including 3 years of experience building and maintaining production data pipelines. Relevant advanced academic experience may substitute for part of the overall experience requirement.
• Strong Python and SQL skills, including the ability to write maintainable, tested, and well-documented code.
• Experience designing reliable ETL pipelines and working with large, heterogeneous datasets, including structured and unstructured data.
• Hands-on experience with AWS, particularly S3, relational databases such as PostgreSQL, and NoSQL databases such as MongoDB.
• Experience with data modeling, schema management, dataset versioning, and automated data quality checks.
• Familiarity with Docker, Kubernetes-based environments, Git, and CI/CD workflows.

• Understanding of data security, access controls, and governance. Strong problem-solving and communication skills, with the ability to work independently and collaborate effectively with data scientists, ML engineers, and product managers. *, * Experience supporting ML datasets, annotation workflows, and reproducible training and evaluation splits.

• Familiarity with Jenkins, ArgoCD, and MLflow.
• Experience with knowledge graphs and ontology engineering. Interest in cybersecurity applications.

Whether you’re an experienced professional or just getting started, your contributions matter at Fortra. If you’re passionate about tackling meaningful challenges alongside talented team members committed to helping each other succeed, all while having lots of fun, we want to hear from you. We offer competitive benefits and salaries, personal and professional development opportunities, flexibility, and much more!