Data Engineer

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About the Role

We’re looking for a Data Engineer who can connect our entire financial + operational ecosystem into one clean, trusted data platform. You’ll build ingestion pipelines, unified models, a lakehouse layer, orchestration workflows, and BI-ready datasets that fuel forecasting, SaaS analytics, and company-wide decision-making. 

Responsibilities

• Build & maintain ingestion pipelines from APIs (QBO, Carta, Forcastr, HRIS, SaaS apps).

• Develop ingestion frameworks using Python, PySpark, Databricks, AWS Glue/EMR.

• Model + blend financial, operational & product data into analytics-ready layers.

• Maintain data lakehouse/warehouse (Delta, Snowflake, Redshift, BigQuery).

• Implement DQ checks, validation, monitoring & reliability frameworks.

• Build batch + streaming pipelines (Kinesis, Kafka, EventBridge).

• Develop ETL/ELT workflows (Airflow, Dagster, Prefect, Step Functions).

• Manage metadata, cataloging & lineage (Glue Catalog, Unity Catalog, dbt docs).

Required Skills

• Strong experience with Python & PySpark.

• Hands-on with AWS data stack (S3, Glue, Lambda, Kinesis, Step Functions, IAM).

• Experience with Databricks or similar Spark ecosystems.

• Skilled in ETL/ELT tools (Airflow, Dagster, Prefect, Meltano).

• Strong SQL modeling: star schemas, dimensional modeling, ELT patterns.

• Experience with Snowflake / Redshift / BigQuery or Delta Lake.

• Understanding of batch vs. streaming architectures.

• BI dataset experience (Looker, Power BI, Mode, Tableau).

Nice-to-Have

• Experience with finance systems (QBO, NetSuite, Carta, Forcastr, Brex, Ramp).

• dbt experience (transformation + semantic modeling).

• Experience with micro-batch, CDC, real-time ingestion.

• IaC (Terraform, CDK, Pulumi).

• Knowledge of SaaS metrics, FP&A workflows.

• ML/analytics engineering experience.

• Multi-tenant SaaS data architecture experience.

What We Offer

• 100% Remote work opportunity.

• Competitive salary based on experience.

• Work on modern, large-scale full-stack applications.

• Collaborative and supportive engineering culture.

• Opportunities for technical growth and leadership.

How to Apply

Apply on LinkedIn with your resume