Data Engineer

About the role

Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems. Data Engineering Lead – Azure Data Platform, Lakehouse

What you'll do

• Personally design, build, and maintain ETL/ELT pipelines and transformation logic for the highest-priority and most complex data integrations — extract
• Write and review transformation logic for incremental loads, change data capture (CDC), deduplication, slowly changing dimensions (SCD)
• Write and review Gremlin graph models and traversal queries in Azure Cosmos DB for Apache Gremlin for knowledge-graph and relationship-heavy use cases
• Write and review SPARQL queries and Turtle (.ttl) ontology/data files for RDF-based knowledge graph and semantic layer work, using a triplestore such as Graphwise GraphDB
• Set enterprise data architecture standards — lakehouse design, medallion layering conventions, incremental-load/CDC patterns, and data-store selection (relational

What you need

• 12–22 years in data science & ai
• SQL
• ETL
• Python
• Spark
• Databricks
• Azure
• Any graduate

Good to have

• Airflow
• AWS
• GCP
• NoSQL
• ERP
• CRM