Analytics Engineer
Core
Design, develop, and maintain data models and transformations to turn raw lakehouse data into clean, validated datasets for analysts and business stakeholders.
Role type
Analytics Engineer (Data Modeling & Quality)
Builds
Clean, validated datasets across bronze/silver/gold layers; automated data quality checks; semantic layer metrics and dimensions.
Domain
Financial services (payments, banking, multi-currency accounts)
Deliverable
production ML models | product features | dashboards & analysis
Required skills
SQL, dbt, Python (pandas), ELT/ETL pipeline design, orchestration (Airflow), data modeling (dimensional/star schema), data quality testing frameworks, Git
Preferred skills
Financial data domain knowledge (payments, acquiring, reconciliation), BI tools (Apache Superset), semantic layer concepts, streaming/CDC technologies (Kafka, Debezium)
Technologies
dbt, Apache Iceberg, Trino, Apache Druid, Snowflake, Redshift, GitLab, Airflow, Great Expectations, Soda, Apache Superset, Kafka, Debezium
Responsibilities
Design and maintain data models and transformations; implement automated data quality checks and validation processes; collaborate with engineers and analysts to translate business requirements; perform sanity and reconciliation checks; contribute to the semantic layer by defining metrics; document data models and quality issues; support analysts with reliable datasets.
Seniority
Mid-level, hands-on IC
