Data Engineer

Data Engineer - Monq

We are looking for brilliant engineers to join our team at Monq. We're pushing the boundaries of AI with next-generation agentic systems that can autonomously negotiate high-value enterprise contracts. We're focusing on a $4.2 trillion market of strategic procurement that has been largely untouched by AI innovation.

Our mission is to transform how enterprises negotiate their most critical deals, turning strategic procurement from a manual, gut-driven process into a competitive advantage powered by intelligent AI agents.

About the Role

Data Engineers are the backbone of Monq. They design, build, and maintain the scalable data infrastructure that powers our autonomous negotiation agents. You'll work closely with AI engineers, product teams, and procurement domain experts to transform complex contract data, vendor information, and negotiation histories into structured, reliable insights that drive intelligent decision-making across our platform.

We're creating the data foundation for AI agents to parse multi-dimensional deal terms, benchmark contract conditions, and optimise negotiation outcomes in high-stakes enterprise environments. This role is an opportunity to work on deeply technical challenges with real-world impact in Fortune 500 procurement operations.

What You Will Do

Build Enterprise Contract Data Infrastructure:

• Design and operate performant, scalable ingestion pipelines processing high-volume contract data, vendor databases, and procurement system integrations from Fortune 500 enterprises
• Create sophisticated data parsing systems for complex legal/procurement documents, multi-dimensional deal terms, and procurement workflow data
• Build real-time data feeds supporting autonomous negotiation agents and strategic decision-making dashboards

Manage Strategic Procurement Data Architecture:

• Define, evolve, and manage data schemas for contract intelligence, vendor benchmarking, and negotiation performance analytics—from raw contract staging to high-quality feature stores for AI agents
• Build comprehensive contract data catalogues ensuring discoverability and lineage tracking for complex procurement datasets
• Design data models that support multi-dimensional optimisation across price, terms, risk, timeline, and relationship factors

Ensure Enterprise-Grade Data Operations:

• Build end-to-end monitoring and observability for mission-critical negotiation data pipelines: owning data quality, latency, completeness, and lineage for $1M+ contract processes
• Champion secure, governed data practices for sensitive enterprise contract information: advanced access controls, secrets management, encrypted data-in-transit/at-rest, and compliance with enterprise security frameworks
• Implement robust audit trails and data governance for high-stakes negotiation processes

Enable AI-Powered Negotiation:

• Collaborate closely with AI, Platform, and Product teams, provisioning datasets, feature tables, and contracts that power autonomous negotiation agents at scale
• Build data infrastructure supporting real-time contract analysis, vendor research automation, and strategic negotiation planning
• Create data pipelines that integrate with major procurement systems (SAP, Oracle, Coupa, GEP) and enterprise workflows

Drive Operational Excellence:

• Continuously improve efficiency and reliability via comprehensive testing, CI/CD automation, cost/performance tuning for enterprise-scale deployments
• Lead incident response and root-cause analysis for data systems supporting critical procurement negotiations
• Optimise data infrastructure costs while maintaining enterprise-grade performance and reliability standards

Requirements

Essential Qualifications:

• Enterprise Data Engineering Expertise: 3+ years building and running data pipelines in AWS or Azure, with experience in enterprise B2B environments and complex data integration challenges
• Advanced Technical Skills: Expert-level programming in Python with focus on production-grade ETL systems; experience with enterprise data at scale
• Modern Data Pipeline Mastery: Deep experience with batch and streaming frameworks (Apache Spark, Flink, Kafka Streams, Beam), including orchestration via Airflow, Prefect, or Dagster

Preferred Qualifications:

• Contract/Document Processing Experience: Background in legal/procurement document processing, contract analysis, or complex unstructured data parsing
• Enterprise Integration Experience: Proven track record integrating with major enterprise systems (ERP, procurement platforms, business intelligence tools)
• AI/ML Pipeline Experience: Experience building data infrastructure that supports machine learning models and AI agents in production

Technical Requirements:

• Data Modelling & Schema Management: Demonstrated expertise designing and evolving schemas for complex enterprise data, including data contracts and comprehensive cataloguing systems
• API & Enterprise Integration: Advanced skills building data ingestion from REST/gRPC APIs, enterprise file systems, message queues (SQS, Kafka), and complex SaaS integrations with robust error handling
• Storage & Query Engines: Strong expertise with RDBMS (PostgreSQL, MySQL), NoSQL systems (DynamoDB, Cassandra), data lakes (Parquet, ORC), and modern warehouse architectures
• Enterprise Security & Compliance: Deep understanding of data encryption, secrets management, RBAC/ABAC, and compliance frameworks relevant to enterprise procurement data
• DevOps & Automation: Expert-level comfort with CI/CD for data systems, infrastructure as code (Terraform), version control, and enterprise deployment workflows

Essential Skills:

• Observability & Quality: Deep familiarity with metrics, logging, tracing, and data quality tools (Great Expectations, Monte Carlo, custom validation suites) for mission-critical systems
• Collaborative Leadership: Experience working closely with AI engineering, platform, and business teams in fast-paced, high-stakes environments
• Enterprise Mindset: Understanding of enterprise software requirements including security, scalability, audit compliance, and integration complexity
• Problem-Solving: Ability to architect solutions for complex, multi-dimensional data challenges in enterprise procurement environments

Work Environment:

• Remote first with quarterly gatherings
• Direct collaboration with Fortune 500 procurement teams
• Annual team retreat - fully-funded off-site focused on AI innovation and team building

Career Growth:

• Visa sponsorship is available for exceptional candidates who complete our recruitment process
• Opportunity to define the future of AI-powered enterprise negotiations
• Direct mentorship from experienced AI researchers and enterprise software veterans

Others:

• No HR organisation
• Minimum 30 days of annual leave and a day off in the month of your birthday
• Flexible working hours as long as we get the things done
• Temporary work from abroad up to 120 days a year (certain limitations apply depending on your nationality/work authorisation status and tax obligations)

Our Interview Process

We run a focused interview process designed for senior data engineering talent:

• Initial Screening (30 minutes) - we only have this stage if we need further clarifications and/or data points about your resume and application form:
• Discussion of your data engineering background and interest in enterprise applications
• Overview of Monq's data infrastructure challenges in procurement and contract processing
• Alignment on career goals and role expectations

Online Assessment/Home Task (45 minutes):

• We assess your technical data engineering skills and cultural fitness; complete this stage at a time that suits you

Technical Architecture Interview (60 minutes):

• Real-world data pipeline challenges from our contract processing and negotiation systems
• System architecture design exercise focused on enterprise data integration
• Discussion of scalability, security, and reliability for mission-critical procurement data

Problem Solving Interview (45 minutes):

• Assessment of your problem-solving approach with a recent complex data challenge we've encountered and solved

Bar Raiser Interview and On-site Collaboration (half a day):

• Join in a discussion with one of our co-founders about the most complex data infrastructure challenges you've solved
• Work directly with our data and AI teams on current contract processing pipeline challenges
• Meet with procurement domain experts and enterprise customer stakeholders
• Experience our culture of rigorous technical development and enterprise focus