Senior Data Infrastructure Engineer

Job summary

The Senior Data Infrastructure Engineer role focuses on building scalable and reliable data infrastructure for AI and data products. Key responsibilities include designing ingestion and processing architectures, creating robust data workflows, and collaborating with machine learning and product engineering teams to productionize AI features. The position requires strong expertise in data engineering, Python, SQL, and cloud platforms such as AWS, GCP, or Azure.

Qualifications

• Strong experience in data engineering or platform engineering in production environments
• Excellent Python skills and solid SQL fundamentals
• Experience building reliable ingestion and transformation pipelines at scale
• Strong understanding of data modeling across structured and unstructured datasets
• Experience with workflow orchestration tools such as Airflow, Dagster, Prefect, Temporal, or equivalent
• Strong cloud engineering experience in AWS, GCP, or Azure, with clear transferability across platforms
• Experience with infrastructure as code and modern deployment practices
• Experience with distributed systems, event-driven patterns, and data-intensive applications
• Familiarity with search, vector, or retrieval systems used in AI-backed products
• Ability to work cross-functionally and act as a technical leader without losing hands-on depth

Responsibilities

• Work closely with machine learning engineers, software engineers, product managers, and leadership to turn raw structured and unstructured data into trustworthy product capabilities
• Design and build scalable batch and streaming pipelines for structured, semi-structured, and unstructured data
• Own the ingestion and processing architecture for documents, text, metadata, and other content sources
• Build robust data workflows for parsing, chunking, enrichment, indexing, and retrieval
• Create the platform foundations for AI products, including orchestration, data quality, observability, lineage, and cost-aware processing
• Design storage patterns across object stores, relational databases, search/vector systems, and where appropriate graph or knowledge-based systems
• Partner with ML and product engineering to productionise AI features, agentic workflows, and retrieval-backed user experiences
• Define data contracts, schema evolution practices, and quality controls across services and teams
• Improve reliability, freshness, and traceability of pipelines that feed customer-facing products
• Contribute hands-on code while helping set engineering standards and mentoring other engineers

Skills

Relocation