Build and evolve ingestion and transformation pipelines on the current stack (Snowflake + dbt + Fivetran), ensuring reliable data for the whole company;
Participate in migrating the legacy architecture (EMR/PySpark/Redshift) to the modern data stack, focusing on operational simplicity and data quality;
Design semantic layers (staging, intermediate, marts) that serve both BI and feature needs for AI models;
Monitor, diagnose and optimize data processing, proactively anticipating issues;
Collaborate with Product, Risk, Finance and Engineering teams to translate business needs into sustainable data solutions;
Use generative AI as a productivity tool in day-to-day work (code generation, documentation, debugging) — this is expected.

Advanced SQL — data modeling, performance tuning, and debugging complex queries;
Experience with ELT/data integration tools (Fivetran, Airbyte or similar);
Experience with cloud data warehouses — Snowflake is current, but Redshift or BigQuery experience also counts;
Knowledge of version control and software best practices (Git, code review, testing);
Ability to read a dataset, question its provenance and advocate for its quality;
Experience with dbt (Core or Cloud) — modeling, tests, documentation;
Familiarity with Snowflake (warehouses, stages, tasks, Snowpipe, Cortex);
Background in AWS (S3, Glue, Athena, EMR) — useful during the transition;
Exposure to AI/ML concepts applied to data: embeddings, feature stores, pipelines for LLMs;
Terraform or other IaC — our data infrastructure is code;
Experience in fintechs or high-data-criticality environments.

Senior Data Engineer

Key skills