Technical Project Manager, Commercial Integrations

QIMA; Paris; On-site; EUR 90,000 - 120,000; Full time

Job summary

QIMA is seeking a seasoned Technical Project Manager for data integration in Paris. You will lead post-acquisition data integration efforts, coordinate across Legal, IT, Finance, Sales, Marketing and Revenue Operations, and drive data quality and governance throughout migration into QIMA systems.

The role emphasizes schema mapping, ETL design, data quality validation, and applying AI/ML tooling to accelerate ingestion and anomaly detection, all while ensuring GDPR compliance and effective

Qualifications

• 5-7 years in technical project management, including data integration or data engineering.
• Hands-on experience with schema mapping, data matching and deduplication, ETL design, and data quality validation.
• Working knowledge of SQL; Python or comparable scripting for data processing and automation.
• Understanding of MDM concepts (data domains, golden records, ownership, lineage) and GDPR considerations.
• Experience managing project workstreams across multiple stakeholder groups.
• Exceptional English-language communication skills with ability to explain technical concepts to non-technical audiences.
• Bonus: post-merger integration experience including exposure to CRM/ERP systems in data integration capacity.
• Bonus: project management certification (PMP, Prince2, or Agile/Scrum).

Responsibilities

• Own project plans, timelines, risk registers, and status reporting for all active integration workstreams; elevate blockers and business decisions proactively.
• Maintain integration documentation, decision logs, and handover notes that enable teams to own the output once integration is complete.
• Coordinate across Legal, IT, Finance, Sales, Marketing, and Revenue Operations to gather requirements, identify dependencies and handoffs, secure milestone sign-off, and manage competing priorities.
• Translate complex data processes into clear, accessible language for non-technical stakeholders at all seniority levels.
• Lead post-acquisition data integration workstreams: discovery, profiling, schema mapping, matching, cleansing, and migration from acquired entities into QIMA systems.
• Define integration plans with milestones, dependencies, and acceptance criteria; identify data quality or process issues early and drive remediations.
• Validate data integrity across source and target systems at each stage; facilitate knowledge-extraction with acquired company experts before systems are decommissioned.
• Build or coordinate data pipelines and ETL processes; apply AI and machine learning tooling to accelerate data ingestion, deduplication, and anomaly detection.
• Design automated quality checks that reduce manual validation while preserving accuracy.
• Apply MDM principles to align acquired data with QIMA standards; define ownership, stewardship, and lineage documentation; ensure GDPR compliance while preserving analytics usability.

Skills

Technical project management

Data integration

Data engineering

SQL

Python

MDM

GDPR compliance

Stakeholder management

Tools

SQL

Python

Job description

Company Description

At QIMA, we are on a mission to help our clients make products consumers can trust. We have developed compliance solutions for testing, inspection and certification (TIC) that enable supply chain agility, sustainability and product innovation. Our services are used by 30,000 businesses globally in the consumer products, agri-food and life sciences industries. Backed by 6,000+ dedicated Qimates worldwide, we bring local expertise and tailored support to every client. Our passion for service has made QIMA an industry leader for customer satisfaction, as highlighted by our exceptional Net Promoter Score (NPS).

What truly sets us apart is our unique culture of relentless care for our clients, and a commitment to offering intuitive solutions that blend deep tech and human intelligence; this is how QIMA continues to disrupt the testing, inspection and certification industry. Qima is driven by client passion and thrives by focusing on agility, integrity, and growing together. After rapid strategic acquisitions, we're at an inflection point as we transition from start-up velocity to scale-up sophistication. We're building the processes, systems, and team to support our next phase of growth while maintaining the entrepreneurial spirit that got us here.

Job Description

QIMA’s rapid growth has added complexity in data, systems, processes, and management structures. Successful integration requires close collaboration between technical, commercial, and core business teams. This role is responsible for driving that collaboration.

Key Responsibilities

Technical Project Management

• Own project plans, timelines, risk registers, and status reporting for all active integration workstreams; elevate blockers and business decisions proactively.
• Maintain integration documentation, decision logs, and handover notes that enable teams to own the output once integration is complete.

Stakeholder Management & Communication

• Coordinate across Legal, IT, Finance, Sales, Marketing, and Revenue Operations to gather requirements, identify dependencies and handoffs, secure milestone sign-off, and manage competing priorities.
• Translate complex data processes into clear, accessible language for non-technical stakeholders at all seniority levels.

M&A Data Integration

• Lead post-acquisition data integration workstreams: discovery, profiling, schema mapping, matching, cleansing, and migration from acquired entities into QIMA systems.
• Define integration plans with clear milestones, dependencies, and acceptance criteria; identify data quality or process issues early and drive well communicated remediations.
• Validate data integrity across source and target systems at each stage; facilitate knowledge-extraction with acquired company experts before systems are decommissioned.

Data Engineering & Automation

• Build or coordinate data pipelines and ETL processes; apply AI and machine learning tooling to accelerate data ingestion, deduplication, and anomaly detection.
• Design automated quality checks that reduce manual validation effort without compromising accuracy.

Master Data Management & Governance

• Apply MDM principles to align acquired data with QIMA master data standards; define ownership, stewardship rules, and lineage documentation for integrated datasets.
• Navigate data protection and privacy requirements throughout integration, ensuring GDPR compliance while preserving data usability for analytics and AI applications.

Qualifications

Required Experience

• 5-7 years in technical project management, including data integration or data engineering, with proven track record delivering complex, multi-stakeholder projects.
• Hands-on experience with schema mapping, data matching and deduplication, ETL design, and data quality validation.
• Working knowledge of SQL; Python or comparable scripting for data processing and automation.
• Understanding of MDM concepts (data domains, golden records, ownership, lineage) and PII regulations (GDPR and equivalent), including the practical balance between compliance and AI usability.
• Experience managing project workstreams across multiple concurrent stakeholder groups spanning commercial and technical functions.
• Exceptional English-language communication skills with proven ability to communicate technical concepts to non-technical audiences in writing and in meetings.
• Bonus: post-merger integration experience, including direct exposure to CRM, marketing automation, or ERP systems in a data integration capacity.
• Bonus: project management certification (PMP, Prince2, or Agile/Scrum).

Desired Characteristics

• Clear Communicator: You can make a data mapping discussion accessible to a CFO and credible to a data engineer, and you know which version to use in which room.
• Disciplined with Detail: Integration work lives and dies in the detail. You catch discrepancies before they reach production and document decisions so they are not repeated.
• Technically Grounded, Commercially Aware: You understand data architecture and frame technical work in terms of the outcomes it enables.
• Comfortable with Ambiguity: Acquired data is rarely clean and processes are rarely documented. You impose structure and clarity without being rigid.
• Collaborative by Default: You coordinate large, cross-functional groups without a reporting line through preparation, clarity, and trust.