- Company Name
- WeBuild-AI
- Job Title
- Data Platform Engineer
- Job Description
-
**Job title**
Data Platform Engineer
**Role Summary**
Design, build, and maintain scalable data architectures that underpin enterprise AI initiatives. Interface with clients to define data strategy, implement cloud‑native platforms (AWS and Azure), and develop data mesh/fabric solutions that support AI workloads. Collaborate with AI engineers to ensure data structures enable advanced ML/AI capabilities and contribute to the Pathway platform and AI agent delivery.
**Expectations**
- Deliver high‑quality data platforms that accelerate AI transformations for global enterprise customers.
- Drive continuous innovation in data architecture, exploring emerging technologies and challenging conventional approaches.
- Communicate complex technical concepts in business‑relevant terms and work effectively with cross‑functional teams.
**Key Responsibilities**
- Assess client data landscapes and formulate strategic roadmaps.
- Design and implement scalable, resilient data platforms across AWS and Azure services (e.g., Redshift, Glue, Synapse, Data Factory, Purview, Fabric).
- Build and maintain data mesh/fabric structures for optimal discoverability, accessibility, and governance.
- Establish governance, control planes, and data quality mechanisms for structured and unstructured data.
- Collaborate with AI engineers to align data models with AI pipelines and advanced analytics.
- Support the Pathway platform development, including AI agents for data governance and quality.
- Prototype and evaluate vector and graph databases (Pinecone, Neo4j, Neptune) for AI use‑cases.
- Develop data integration solutions (ETL, streaming via Glue, MSK, Kinesis, Kafka).
- Leverage AI developer tools (e.g., Cursor, GitHub Copilot) to enhance productivity.
**Required Skills**
- Strong experience with AWS data services (DataZone, Bedrock, Redshift, Glue) and/or Azure data services (OpenSearch, OpenAI, Fabric, Purview, Data Factory, Synapse).
- Proficiency in Python for data processing, pipeline creation, and automation.
- Expertise in building scalable, cloud‑native data platforms and containerisation (Docker, Kubernetes).
- Knowledge of vector and graph databases.
- Understanding of data mesh/fabric patterns and modern data architecture.
- Familiarity with AI/ML workflows and their data requirements.
- Experience with API specifications and data integration across ETL and streaming services.
- Strong problem‑solving, critical thinking, and collaborative communication skills.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Data Engineering, or related field (or equivalent experience).
- Professional certifications in AWS/Azure data services, data engineering, or AI (e.g., AWS Data Analytics Specialty, Azure Data Engineer Associate) preferred.