# Senior Specialist - Industry 4.0 & Manufacturing Data at Deep DSG > Hybrid · Ireland · Senior · Software engineering · open. Source: https://alderhq.app/jobs/recruitee%3Adeepdsg%3A2649187 (read 2026-11-08). ## Facts (observed) - Company: [Deep DSG](https://alderhq.app/companies/deep-dsg) - Location text: Cork, Munster, Ireland - Where it can be done from (inferred): Hybrid · Ireland (evidence: "Location: Cork, Munster, Ireland") - Posted: 2026-06-22 (140 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-08 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://deepdsg.recruitee.com/o/senior-specialist-industry-40-manufacturing-data-3 - Skills listed (inferred): Python, SQL ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Ireland, n 13): p25 €110k, median €113k, p75 €113k ## Description (the employer's text) ### Senior Data Consultant We are seeking a Senior Specialist – Industry 4.0 & Manufacturing Data to act as a technical authority and strategic advisor across digital manufacturing initiatives. This role blends deep technical expertise, regulated-industry rigor, and client-facing leadership, supporting both delivery excellence and the growth of Deep Digital’s Industry 4.0 services. Location: Remote - Ireland ### Main Responsibilities Act as a trusted senior advisor to clients on Industry 4.0, manufacturing data, and digital transformation Lead development of digital transformation strategies and roadmaps, including: Manufacturing Data Platforms & Historians Unified Namespace (UNS) and event-driven architectures OT/IT integration and cloud/hybrid data strategies Guide technology selection and architecture across: Data integration, streaming, and contextualisation platforms MES, LIMS, QMS, CMMS, and Enterprise integrations Control systems and OT platforms Advanced analytics, AI/ML, and digital twin use cases Embed data, validation, and governance strategies into project designs in collaboration with internal engineering teams and client stakeholders Lead small, multidisciplinary teams delivering projects from concept through design, validation, and handover. ### Job benefits Hybrid working 22 days Annual Leave 5% Pension Paid Sick days Income Protection Life Insurance Professional development ### Requirements ### Qualifications Bachelor’s or master's degree in data science, Computer Science, Statistics, or a related field and 6+ years of relevant experience. Where a degree is not held—significant professional experience that demonstrates the ability to excel in this role. Deep experience/understanding across Industry 4.0 domains, including: Control systems, automation, and OT environments. Manufacturing data historians and platforms IT/OT integration, networking, and cybersecurity Cloud and hybrid infrastructure Vertical and horizontal system integration (MES–ERP–Enterprise) Strong understanding of: Data warehousing concepts, ETL/ELT processes, and data governance models ISA-88 and ISA-95 standards for batch and manufacturing operations FAIR data principles and data integrity requirements (ALCOA+) Solid experience working within GAMP 5 frameworks and Computer Systems Validation (CSV) in regulated manufacturing environments ### Preferred Skills Excellent verbal, written, and presentation skills, with the ability to communicate complex concepts to both technical and executive audiences Demonstrated ability to manage multiple concurrent projects, prioritise effectively, and meet deadlines Strong problem-solving skills with high attention to detail Understanding of ontologies and semantic models and their role in data standardisation and interoperability Experience with data visualisation or analytics tools (e.g. Power BI, Tableau, Grafana) Hands-on experience with SQL, Python, R, or similar data-focused programming languages Exposure to PAT, real-time release, predictive analytics, or closed-loop control use cases --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Adeepdsg%3A2649187 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.