Principal Data Engineer

Black Duck; Belfast City District; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Black Duck in Belfast, UK, seeks a senior data platform engineer to design and build cross-product data services from a single governed data plane. You will own ingestion, data quality, and data APIs, enabling ML workflows and secure data accessibility.

Bring extensive experience with SQL, Python, AWS/GCP, and data modeling. You will collaborate with product, engineering, and security teams in a hybrid work environment and contribute to data contracts and reliability across the org.

Qualifications

• Significant experience building and operating production data platforms at scale, including on-call and operational ownership.
• Strong SQL and 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).
• Experience with both operational databases (RDS-style) and analytics stores (columnar/OLAP), including performance tuning.
• Strong data modeling ability, including schema evolution and conformed dimensions.
• Track record delivering data products that other teams depend on, with clear contracts and reliability expectations.
• Ability to balance latency, accuracy, cost, and security without brittle complexity.
• Experience with lakehouse patterns and open table formats, including governance and table maintenance.
• Experience with orchestration and streaming systems used in production, and backfill management.
• Familiarity with ML data needs (training/serving splits, feature-ready datasets).

Responsibilities

• 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.

Skills

SQL

Python

AWS

Google Cloud

RDS

OLAP

Data modeling

Data contracts

Data pipelines

Stakeholder collaboration

Job description

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)

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.