Data Engineer

pAre you a senior Data Engineer who wants to leverage data engineering, MLOps, and architecture skills to build production-grade data and AI platforms with real-world impact? At WCC, we build high-impact, mission critical HR tech solutions used by governments and public institutions across the globe to help millions of people find suitable jobs today and prepare for the labor market of tomorrow. We are expanding our team with a senior Data Engineer to help us responsibly build scalable data and AI platforms that turn real-world client data into reliable product capabilities. /ppThis is a hands-on role where you will work with cross-functional teams to design, build, deploy, and operate data and AI platform capabilities for solutions that impact and improve the lives of millions of people around the world. You will combine strong engineering skills with pragmatic judgment and ownership over production outcomes, ensuring that we do the right things and do them right. /ppstrongWhat you’ll do /strong /ppstrongData platforms, data pipelines, databases, and data products /strong /pulliDesign, build, and operate scalable data platforms, databases, and pipelines that ingest, transform, validate, and connect data from multiple internal and external sources, including messy, incomplete, and heterogeneous real-world client data. /liliDesign and optimize relational, analytical, operational, and vector-oriented data stores for large-scale workloads, semantic search, matching, recommendations, and other AI-enabled capabilities. /liliCreate reusable data products, canonical data models, curated datasets, and feature-ready assets for Data Scientists, product teams, implementation teams, and customer-facing applications. /liliBuild in data quality, metadata, observability, and lineage so teams can trust the data and understand where it came from, how it changed, and how it is being used. /li /ulpstrongCloud infrastructure and operational reliability /strong /pulliDesign, deploy, and maintain cloud-native data and AI platform components on AWS, using automation and infrastructure-as-code wherever possible. /liliWork hands-on with storage, compute, networking, access control, secrets management, monitoring, logging, CI/CD, orchestration, and deployment automation. /liliEnsure data and AI workloads are secure, scalable, cost-effective, maintainable, and supported by pragmatic architectural trade-offs across environments. /li /ulpstrongMLOps, AI governance, and responsible operation /strong /pulliEnable Data Scientists and AI engineers to turn experiments, models, prompts, embeddings, and retrieval pipelines into reliable production services. /liliSupport automated workflows for model training, validation, deployment, versioning, monitoring, retraining, lifecycle management, and lineage across datasets, features, models, prompts, embeddings, evaluations, deployments, and production outcomes. /liliImplement monitoring and alerting for data quality issues, model performance, data drift, concept drift, embedding drift, and unexpected changes in production behavior. /liliTranslate AI governance requirements into workable platform capabilities such as audit trails, approval gates, access controls, evaluation workflows, release controls, and operational dashboards. /li /ulpstrongCollaboration and technical ownership /strong /pulliWork closely with Data Scientists, Architects, DevOps engineers, Product Owners, and project teams to translate data and AI needs into maintainable technical solutions. /liliAct as a senior technical sparring partner on data architecture, platform design, MLOps, AI governance, operational readiness, and production support, with a strong focus on practical decisions that keep solutions understandable, supportable, and reliable over time. /liliDocument data flows, model flows, platform designs, operational procedures, and architectural decisions so solutions can be understood, supported, audited, improved, and kept reliable in production. /liliTake ownership of production outcomes: not just building pipelines and services, but ensuring they keep working, remain understandable, and support the people and products that depend on them. /li /ulpstrongWork conditions /strong /pulliHybrid work setup when not traveling (60% in our Utrecht office, 40% from home). /liliTravel internationally as required (incidentally, depending on project needs). /li /ulpstrongWhat you bring /strong /ppstrongCore requirements /strong /pulliA completed degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Econometrics, or another quantitative or technical field; a Master’s degree is a plus. /liliAt least 5 years of professional experience in data engineering, platform engineering, MLOps, cloud engineering, or a closely related role. /liliStrong experience designing, building, and operating scalable data platforms, pipelines, data lakes, or data products in production environments, including ETL development, workflow orchestration, data quality controls, metadata, lineage, and operational monitoring. /liliStrong architectural judgment and a pragmatic, hands-on mindset, with the ability to balance speed, scalability, cost, security, governance, reliability, and maintainability while taking ownership in complex technical environments. /liliExperience supporting production AI or machine learning services with monitoring, versioning, lifecycle management, reproducibility, deployment traceability, lineage, drift detection, audit trails, access control, and operational controls across datasets, features, models, and deployments. /liliStrong hands-on experience with AWS-based infrastructure and services, Python, Docker, infrastructure-as-code, CI/CD, and production-grade deployment practices for data or platform workloads. /liliAbility to turn messy, incomplete, inconsistent, or fast-changing real-world data into reliable, usable, and well-documented assets. /liliAdvanced SQL skills and substantial experience with database design, query optimization, canonical data models, scalable schema design, and modeling for complex business domains. /liliStrong collaboration and communication skills, with the ability to work effectively across Data Science, DevOps, Architecture, Product, implementation, Security, and Privacy stakeholders. /liliProfessional-level English, both written and spoken. /li /ulpstrongPreferred qualifications /strong /pulliExperience with AWS data, compute, networking, security, monitoring, and deployment services, such as S3, Lambda, Glue, Step Functions, EventBridge, Athena, EMR, RDS, Redshift, DynamoDB, ECS, ECR, EC2, VPC, IAM, CloudWatch, CloudTrail, Systems Manager, Secrets Manager, KMS, API Gateway, or AWS CDK. /liliExperience with Kubernetes, Terraform, Bitbucket Pipelines, AWS Lake Formation, AWS DataZone, AWS Glue Data Catalog, or lakehouse architectures. /liliExperience with MLflow, Amazon SageMaker, AWS Bedrock, feature stores, model registries, experiment tracking, evaluation stores, model monitoring platforms, drift detection, or AI observability tools. /liliFamiliarity with lakehouse architectures, Data Vault Modeling, data mesh concepts, data contracts, master data management, schema registries, data lineage frameworks, metadata management, or enterprise data cataloging. /liliExperience with vector databases, embedding storage, vector indexing techniques, retrieval-augmented generation architectures, or semantic search platforms. /liliFull-stack software development experience or experience building APIs and services that expose data or AI capabilities to products. /liliFamiliarity with frameworks such as GDPR, the EU AI Act, ISO 27001, and ISO 42001. /liliAbility to translate data governance, AI governance, compliance, and risk management needs into pragmatic platform capabilities such as approval gates, audit trails, evaluation workflows, release controls, and post-deployment monitoring. /liliAWS certifications are a plus. /liliExperience working with public-sector, HR tech, labor market, staffing, or other socially impactful data domains is a plus. /li /ul #J-18808-Ljbffr