Principal Data Engineer
- Your enthusiasm could energize the team during brainstorming sessions for new data products and platforms.
- You bring creative ideas for making data accessible and user-friendly, which could improve self-service features.
- Strong people skills help in partnering with diverse stakeholders across product and engineering.
- However, the role demands heavy technical focus—Python/SQL coding, cloud infrastructure, and data modeling—which may not suit your strengths.
- Operational ownership, on-call reliability, and meticulous data quality checks are likely to feel tedious and draining.
- The need for sustained attention to detail and long-term planning for schema evolution could be a struggle.
Black Duck Software, Inc. helps organizations build secure, high-quality software, minimizing risks while maximizing speed and productivity. Black Duck, a recognized pioneer in application security, provides SAST, SCA, and DAST solutions that enable teams to quickly find and fix vulnerabilities and defects in proprietary code, open source components, and application behavior. With a combination of industry-leading tools, services, and expertise, only Black Duck helps organizations maximize security and quality in DevSecOps and throughout the software development life cycle.
What you’ll do
- Lead the design and build-out of cross-product data services for multiple product lines from one governed data plane.
- Define the “customer data plane” model: canonical customer identifiers, shared dimensions, and consistent facts used across products.
- Build and operationalize ingestion patterns for batch, streaming, and event data, with repeatable onboarding for new sources.
- Own the operational playbook for data reliability: data contracts, quality checks, lineage, monitoring, and incident response.
- Implement and run access methods that make data usable: curated datasets, secure query interfaces, and product-ready data APIs where needed.
- Productize customer-facing data products (datasets, metrics, exports, and feeds) with versioning, documentation, and clear ownership.
- Design data models that fit both operational systems (RDS) and analytics stores (columnar/OLAP), including performance and cost tuning.
- Ensure data products also power ML workflows: trusted training datasets, feature-ready outputs, and consistent definitions for decision-making.
- Enable AI automation by delivering reliable, low-latency, governed data products that can be used safely in automated workflows.
- Partner closely with product, engineering, and security stakeholders to align data products to roadmap priorities and customer outcomes.
- Raise the technical bar through architecture reviews, standards, and mentoring—while staying hands-on in key systems.
Required
- Significant experience building and operating production data platforms at scale, including on-call and operational ownership.
- Strong SQL skills and strong Python skills, used to build pipelines, services, and automation.
- Hands-on experience running cloud systems on AWS and Google Cloud (IaaS level: compute, storage, networking, IAM).
- Practical experience with both operational databases (RDS-style) and analytics stores (columnar/OLAP), including performance tuning.
- Strong data modeling ability, including schema evolution, conformed dimensions, and “one source of truth” metric definitions.
- Track record of delivering data products that other teams or customers depend on, with clear contracts and reliability expectations.
- Ability to make sound engineering tradeoffs across latency, accuracy, cost, and security without creating brittle complexity.
- Experience with lakehouse patterns and open table formats (or similar), including governance and table maintenance.
- Experience with orchestration and streaming systems used in production (batch + real-time), and managing backfills safely.
- Familiarity with ML data needs (training/serving splits, feature-ready datasets, evaluation datasets) and AI-adjacent workflows.
Preferred
- Experience building self-service data platforms (catalog, discoverability, access controls) used by multiple teams.
- Experience in regulated or security-sensitive environments, including retention, auditing, and data access controls.
Work model, location & travel
- Location: Belfast, UK
- Reports to: VP of Data Engineering
- Work model: Hybrid (details TBD)
- Collaboration hours: Flexible; overlap with UK and US time zones
- Travel: Minimal
Black Duck is an equal opportunity employer. We consider all applicants for employment without regard to race, color, national origin, religion, sex, gender identity or expression, age, disability, sexual orientation, veteran or military service status, or any other characteristic protected by applicable law. Black Duck complies with all applicable laws prohibiting employment discrimination in every jurisdiction where it operates and provides reasonable accommodations to individuals with disabilities in accordance with applicable law.
Best personality fits for this role
Your strategic mindset and long-term planning align perfectly with designing a governed data plane and cross-product services, ensuring scalable, future-proof systems.
Read the INTJ career guide →Your deep intellectual curiosity and love for system design are perfect for architecting the customer data plane and ensuring robust data modeling schema evolution.
Read the INTP career guide →Your natural leadership and drive to raise technical bars match the role's expectation to mentor and lead architecture reviews.
Read the ENTJ career guide →