Data Engineer
Required skills
Strong programming with Python and SQL. Comfortable building from scratch and improving existing code. Expertise in data modeling and warehousing, including dimensional modeling and performance tuning. Experience designing and operating ETL and ELT pipelines with tools like Airflow or Dagster, plus dbt for transformations. Hands-on with batch and streaming systems such as Spark and Kafka, and with Lakehouse or warehouse tech on AWS or GCP. Proficiency integrating third-party APIs and datasets, ensuring reliability, lineage, and governance. Familiarity with AI data needs: feature stores, embedding pipelines, vector databases, and feedback loops that close the gap between model and outcome. High standards for code quality, testing, observability, and CI. Comfortable with Docker and modern cloud infra.
Technologies
Python, SQL, Airflow, Dagster, dbt, Spark, Kafka, AWS, GCP, Docker, cloud infra.
Responsibilities
Treats data as a product and ships improvements that users feel. Moves fast without breaking trust. You value contracts, schemas, and backward compatibility. Owns problems across the stack, from ingestion to modeling to serving. Communicates clearly with ML engineers, analysts, and business partners. Experiments, measures, and iterates. You set measurable SLAs and keep them green. Sees ambiguity as a chance to design the standard everyone else will follow.
Seniority
Young talent ready to go all in.
Domain
AI, influencer marketing, data engineering.