Main Responsibilities

? As part of the AI & Data team, design and build the data structures, schemas, and models

that AI Engineers depend on for training, feature engineering, and inference.

? Develop and orchestrate scalable data pipelines on Databricks (Spark, Delta Lake) and load

curated, analytics-ready data into Snowflake.

? Own data ingestion, transformation (ELT/ETL), and storage across the Azure cloud (e.g.

ADLS, Data Factory, Event Hubs/Synapse), including structured, semi-structured, and

unstructured data such as text, images, and video.

? Dock machine-learning and computer-vision models into the data pipelines, and design the

data flow that feeds and consumes those AI services.

? Sync curated lakehouse data into Lakebase (managed Postgres) for low-latency serving,

manage change-data-capture back into Delta tables, and support online feature stores and

agent state for AI Engineers.

? Build and maintain API integrations and automated data ingestion from internal systems

and external third-party sources.

? Monitor pipeline performance, reliability, and cost; troubleshoot failed jobs and optimize

Snowflake and Databricks workloads.

? Implement data quality, validation, and lineage, and document the data dictionary and ETL

processes.

? Partner with AI Engineers and stakeholders to translate model and business requirements

into extensions of the data platform.

Skills, Qualifications & Education

? Bachelor’s degree in Computer Science, Data Engineering, or a related field.

? At least 4 years of work experience in data engineering or a similar data-focused role.

? Hands-on production experience with Databricks (Apache Spark, Delta Lake, notebooks,

workflows).

? Hands-on production experience with Snowflake (data modeling, performance tuning,

access control, cost management).

? Solid experience with Microsoft Azure data services (e.g. ADLS, Data Factory, Event Hubs /

Synapse).

? Experience with PostgreSQL and OLTP databases; familiarity with Lakebase (Databricks

managed Postgres) is a strong plus.

? Working knowledge of JavaScript / TypeScript, used for data APIs, microservices, or app

facing integrations.

? Strong expertise in SQL (will be tested during the recruitment process).

? Robust Python literacy, especially for data handling and pipeline development.

? Comfortable working with both structured and unstructured data; does not shy away from

troubleshooting failed ETL processes or API integrations.

? Outstanding data-structure and data-modeling design skills.

? Working knowledge of machine-learning, NLP, or computer-vision workflows is a plus.

? Experience with dbt, Airflow, or Databricks Workflows, and with CI/CD and infrastructure

as-code, is a plus.

? Strong ability to translate ideas between technical and non-technical audiences.

? Curious, collaborative, self-motivated, and organized; able to run multiple projects against

tight deadlines.