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; semantic layer metrics and dimensions for BI and reporting.
Domain
Financial services / Payments / Banking
Deliverable
production ML models | product features | dashboards & analysis
Required skills
SQL, dbt, Python (pandas), ELT/ETL pipeline design, orchestration (Airflow), data lakehouse/warehouse technologies (Apache Iceberg, Trino, Snowflake, Redshift), data quality testing frameworks (Great Expectations, Soda), Git (GitLab), dimensional modeling (star schema)
Preferred skills
Bridging data engineering and analytics, financial data domains (payments, acquiring, reconciliation), BI tools (Apache Superset), streaming/CDC technologies (Kafka, Debezium)
Technologies
dbt, Airflow, Apache Iceberg, Trino, Apache Druid, Snowflake, Redshift, GitLab, 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 into data models; perform sanity and reconciliation checks to resolve data inconsistencies; define and maintain consistent metrics and dimensions for the semantic layer; document data models and quality issues to improve governance; support analysts by providing reliable datasets.
Seniority
Mid-level, hands-on IC