Data Engineer

Infosys is hiring Data Engineer

Required Qualifications:
Candidate must be located within commuting distance of Calgary or be willing to relocate to the area. This position may require travel in Canada.
Bachelor's degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
At least 4 years of Information Technology experience.
Candidates authorized to work for any employer in the Canada without employer-based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role at this time.

Required Skills

6+ years of overall technology experience with 3+ years in Contact Center and Conversational AI architecture.
Data Pipelines: Build ETL flows for structured/unstructured data, ensuring normalization, deduplication, and semantic consistency.
Vector Infrastructure: Manage pgvector, Azure AI Search, Redis vector indexing, and hybrid search layers.
Data Governance: Implement zero-trust access, privacy controls, and compliance within AI context pipelines.
Real-time Processing: Build event-driven architectures that continuously refresh embeddings and indexes. Required Qualifications
Deep experience with distributed data systems, SQL, and orchestration tools.
Experience tuning high-throughput database infrastructure.
Knowledge of Google's GECX is a plus.
Familiarity with chunking strategies and embedding models. Skillset Requirements
ETL & Data Modeling: Designing pipelines for structured/unstructured data, normalization, deduplication, and semantic consistency.
Vector Databases: pgvector, Redis, Azure AI Search, hybrid search, and index optimization.
Distributed Data Systems: Kafka, Spark, Flink, or similar event-driven architectures.
Data Governance: Zero-trust access, privacy controls, compliance, and auditability.
Real-time Embedding Updates: Event-driven refresh pipelines for RAG and agent memory systems.
Chunking & Embeddings: Semantic chunking, metadata tagging, and embedding model selection.
Search Infrastructure: BM25, hybrid search, inverted indexes, and ranking algorithms.
Performance Tuning: High-throughput read/write optimization.
Data Quality & Lineage: Validation, schema enforcement, and lineage tracking (e.g., Great Expectations, OpenLineage).

The job may also entail sitting as well as working at a computer for extended periods of time. Candidates should be able to effectively communicate by telephone, email, and face to face.