Data Engineer

Role Overview

The Data Engineer in our AI & Data team will be responsible for designing and building the data structures and pipelines our AI Engineers rely on across Azure, Snowflake, Databricks, and Lakebase. The primary mission is to enable the AI Engineering team by translating machine-learning and computer-vision workflows into reliable, well-modelled, and cost-effective data foundations.

Responsibilities

• Design and build data structures, schemas, and models for training, feature engineering, and inference.
• Develop and orchestrate scalable data pipelines on Databricks (Spark, Delta Lake) and load data into Snowflake.
• Own data ingestion, transformation (ELT/ETL), and storage across Azure (ADLS, Data Factory, Event Hubs/Synapse).
• Dock machine-learning and computer-vision models into data pipelines and design data flows for AI services.
• Sync curated lakehouse data into PostgreSQL for low-latency serving and manage change-data-capture.
• Build and maintain API integrations and automated data ingestion from internal and external sources.
• Monitor pipeline performance, reliability, and cost while optimizing Snowflake and Databricks workloads.
• Implement data quality, validation, and lineage.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, or a related field.
• At least 4 years of work experience in data engineering.
• Hands-on production experience with Databricks (Apache Spark, Delta Lake).
• Hands-on production experience with Snowflake.
• Solid experience with Microsoft Azure data services.
• Experience with PostgreSQL and OLTP databases.
• Working knowledge of JavaScript / TypeScript.
• Strong expertise in SQL and robust Python literacy.

Skills

• Databricks
• Snowflake
• Azure
• Python
• SQL

Nice to Have

• Working knowledge of machine-learning, NLP, or computer-vision workflows.
• Experience with dbt, Airflow, or Databricks Workflows.
• Experience with CI/CD and infrastructure-as-code.