Senior Data Engineer

About the role:

We’re rebuilding Bosta’s data platform end-to-end: from MongoDB at the source, through a governed semantic layer that LLM-native tools (NL-to-SQL agents, AI analysts, embedded copilots) can sit on top of safely and cheaply. You will help own that rebuild.This is a hands-on role. You’ll set patterns, write the foundational code, and ship across the stack - from CDC and ingestion through dbt, the semantic layer, and the interfaces that BI tools and AI agents consume.

Job Responsibilities

• End-to-end pipeline work: MongoDB CDC → ingestion → lakehouse → warehouse → dbt → semantic layer → BI/AI consumers
• Co-ownership of architecture decisions with the Data Engineering Lead
• CDC from production MongoDB without degrading operational DB performance; ingestion patterns that make adding a new source a config change, not a project
• Orchestration that’s observable end-to-end (Airflow, Dagster, or Prefect)
• The dbt project: structure, conventions, tests, contracts, exposures, CI
• The semantic / metrics layer (dbt Semantic Layer, Cube, or equivalent) - one canonical definition per business metric
• LLM-readiness: column-level documentation, PII tagging, query cost guardrails, materialized metric tables, and evals on AI-generated SQL
• Migration of existing logic out of the Tableau and Metabase sprawl into modeled, governed sources

Job Qualifications

• 4+ years across data engineering and/or analytics engineering - you’ve spent meaningful time on both sides
• Comfort spanning the stack: comfortable shipping a Debezium connector one week and a dbt mart the next
• Deep dbt and SQL - you’ve owned a non-trivial project, not just contributed to one
• Production CDC experience (Debezium, Kafka Connect, Airbyte, or hand-rolled) against operational databases - bonus if that database was MongoDB
• A cloud warehouse you know deeply (Redshift, Snowflake, BigQuery, or Databricks)
• Strong Python; comfortable in Linux, infra-as-code, and CI/CD
• Working understanding of how LLM tooling (RAG, NL-to-SQL, embedded agents) consumes a data platform - and what breaks when the platform isn’t ready
• Strong opinions on modeling, lightly held; bias toward observability

Bonus:

• MongoDB schema evolution at scale
• Production semantic-layer rollouts (dbt Semantic Layer, Cube, LookML, MetricFlow)
• Lakehouse formats (Iceberg, Delta, Hudi) or streaming experience (Kafka, Flink, Kinesis)
• Data catalog / lineage tooling (DataHub, Atlan, Collibra)
• Logistics, marketplaces, or other operationally heavy domains
• Evals on AI-generated SQL or analytical reasoning