At a Glance
- Tasks: Lead the design of innovative data services and operationalise data reliability.
- Company: Join Black Duck Software, a pioneer in application security and data solutions.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with flexible hours and minimal travel.
- Why this job: Make a real impact by building secure, high-quality data products that drive innovation.
- Qualifications: Significant experience in data platforms, strong SQL and Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
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 Dev Sec Ops 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 (Iaa S 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.
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Principal Data Engineer employer: Blackduck
At Black Duck, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our Senior Vulnerability Manager role not only provides the opportunity to lead critical security initiatives but also encourages professional growth through mentorship and cross-team collaboration. With a commitment to diversity and inclusion, we ensure that every employee feels valued and empowered to make a meaningful impact in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Blackduck!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Blackduck.
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We think you need these skills to ace Principal Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Blackduck, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Blackduck. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Blackduck
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Blackduck!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
