We’re looking for a talented Data Engineer to join our AI team and build the data foundations that power our AI systems at scale. As a key member of the AI team, you’ll be responsible for designing, building, and operating the data layer that enables our AI models and agents to perform in production. This includes data ingestion, storage, transformation, monitoring, and defining the data strategy that ensures our AI applications receive high-quality, reliable, and timely data.
We’re seeking an ambitious Data Engineer who is passionate about building robust data infrastructure, working with large-scale and complex datasets, and partnering closely with AI engineers and researchers to translate model needs into production-grade data systems.
WHAT YOU WILL DO
Design, build, and operate scalable data pipelines (batch + streaming) that power Vega’s AI systems in production.
Own the end-to-end data lifecycle: ingestion, storage, transformation, serving, and continuous improvement.
Build and maintain the data layer from raw data to semantically accessible data that enables AI models and agents to perform reliably at scale.
Ensure high data quality and high-standard operation through monitoring, alerting, and validation checks.
Work with modern storage systems (data lakes, warehouses, relational, graph, and vector databases) to support diverse AI workloads.
Partner closely with AI engineers and researchers to translate model requirements into production-grade data infrastructure.
Define and evolve the data strategy to for AI applications.
Requirements
WHAT YOU WILL BRING
4+ years of professional experience in data engineering or ML infrastructure roles.
Strong experience designing and building data pipelines (batch and streaming) for large-scale production systems.
Hands-on experience with data storage systems such as data lakes, data warehouses, relational databases and graph databases.
Proven ability to build reliable, observable, and scalable data infrastructure, including monitoring, alerting, and data quality checks.
Demonstrated ownership across the full data lifecycle from ingestion and modeling, to serving, monitoring, and continuous improvement.
Ability to work independently, managing priorities effectively in a fast-paced, product-driven environment, according to a dynamic data strategy.
Experience with modern data and infrastructure technologies such as DuckDB, dbt, Temporal, Trino, Spark, PostgreSQL, PGVector Neo4j, Datadog, Python, Go, Docker, and Kubernetes.
Experience working with ML models and framework as part of data pipelines (e.g. text embedding models, vector databases, semantic search algorithms)
NICE TO HAVE
Experience building data infrastructure to support ML/AI systems, including feature extraction pipelines for downstream ML models and inference-time data access.
Background in working with high-volume or complex data sources such as logs, events, telemetry, or security data.
Familiarity with modern cloud platforms, preferably AWS, and cloud-native data tools.
Experience collaborating closely with ML/AI engineers to translate model and research requirements into scalable data solutions.
