Senior Data Engineer — AI Analytics Platform

RevSpring; Boston (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

RevSpring seeks a Senior Data Engineer - Analytics to design and manage data pipelines feeding BigQuery and Redshift data stores. The role emphasizes data integrity, scalable architectures, and collaboration with product and engineering teams.

You will own the data stack, implement ETL with Python/SQL, and build analytic semantic layers with Looker/PowerBI/Tableau, while leveraging AI tools to boost developer productivity.

Qualifications

• Experience designing data pipelines to ingest information from various systems into data warehouses.
• Proven track record owning data integrity end-to-end from source to consumption.
• Experience with ETL tools and languages (Python, SQL, Airflow, DBT).
• Experience creating analytic semantic layers and dashboards (Looker/PowerBI/Tableau).
• Ability to leverage AI tools to boost developer productivity.
• Experience with Postgres, BigQuery, Redshift and cloud platforms (AWS/Azure/GCP).
• Experience in developing large-scale data models and complex pipelines.

Responsibilities

• Design and own data pipelines to feed BigQuery/Redshift data warehouses.
• Own code quality, refactoring, and evolving architecture.
• Actively code to unblock technical and cross-team challenges.
• Participate in architecture reviews and documentation for multiple products.
• Communicate with stakeholders about the product and roadmap.
• Provide feedback and participate in pair programming.

Skills

Data pipelines

Data integrity

AI tooling

Problem solving

Education

BS Degree

Tools

BigQuery

Redshift

Postgres

Python

SQL

Airflow

DBT

S3

Airbyte

Confluent

Looker

PowerBI

Tableau

AWS

Azure

GCP

Job description

RevSpring seeks a Senior Data Engineer - Analytics to design and manage data pipelines feeding BigQuery and Redshift data stores. The role emphasizes data integrity, scalable architectures, and collaboration with product and engineering teams.

You will own the data stack, implement ETL with Python/SQL, and build analytic semantic layers with Looker/PowerBI/Tableau, while leveraging AI tools to boost developer productivity.