Senior Data Engineer

About Quandri

What you'll do

• Own the Databricks data lake, dbt models, medallion layers, incremental and backfill strategy, partitioning, freshness, and quality monitoring.
• Build CDC and streaming ingestion from HubSpot, Langfuse, Postgres, and DynamoDB, with idempotency and deduplication.
• Own data services, schemas, migrations, versioned APIs, provenance, audit trails, tests, and observability.
• Build AI data infrastructure, including embedding pipelines, vector stores, retrieval knowledge bases, feature stores, and LLM observability.
• Develop cloud databases with the Infrastructure team, improve data retrieval, and optimize analytics dashboards.
• Maintain data management and security policies; collaborate across technical and business teams and mentor engineers.

Requirements

• At least 4–6 years of professional data engineering experience.
• Experience owning databases, data pipelines, and backends, with emphasis on data management and system integration.
• SQL proficiency is required; Python is preferred.
• Experience with multi-tenant systems, hard isolation requirements, and PII or other regulated data.
• Proficiency in data modeling; examples include medallion architecture, star schema, and Snowflake schema.
• Hands-on experience with AWS, Azure, or GCP.
• Experience with pipeline orchestration or CDC tools; examples include dbt, Databricks Workflows, Airflow, Prefect, Dagster, and AWS Step Functions.
• Proficiency with data visualization tools and data governance; experience building or supporting AI products.

Bonus qualifications

• A bachelor's or master's degree in a related technical discipline, or equivalent experience, is a bonus.
• Leadership communication experience and experience with vector databases such as Pinecone or AWS Bedrock Knowledge Bases are bonuses.

Company principles