Senior Data Engineer

Reach Financial

18h•$150,000 - $200,000

About The Position

As a Senior Data Engineer at Reach, you’ll work alongside the existing Data Engineering team on the data platform modernization effort. You’ll contribute to the broader data practice by building and maintaining pipelines, data models, and transformations as Reach modernizes its data stack. You’ll help establish scalable, reliable, and consistent data engineering practices across the organization, supporting cohesive data design and management within the Engineering organization. The role involves developing and managing data pipelines, data models, schemas, and other data artifacts; applying engineering best practices; collaborating across Data, Software, and Platform Engineering; and translating organizational data requirements into technical solutions. You’ll also evaluate emerging tools and technologies, integrate and deploy pipelines through shared CI/CD and Infrastructure as Code solutions, monitor and troubleshoot pipelines, and maintain documentation. The role requires at least six years of hands-on data engineering experience and production experience with technologies and practices including dbt, Snowflake, data architecture, orchestration, testing, CI/CD, and operational database management. Preferred experience includes preparing data for BI tools, Tableau CRM Analytics Data Pipelines, Sigma Computing, Fivetran Activations, and work in financial services or regulated environments.

Requirements

At least 6 years of hands-on data engineering experience, working with technologies such as dbt (Core or Cloud), Snowflake, Fivetran, Amazon RDS, and PostgreSQL.
Strong proficiency in dbt development best practices and coding conventions, with hands-on experience designing and implementing Medallion data architecture (Bronze, Silver, and Gold layers) in production.
Extensive knowledge of SQL and NoSQL data stores.
Experience with data pipeline orchestration tools such as Airflow, Dagster, or similar platforms.
Experience implementing automated data quality checks, dbt testing, and data validation frameworks.
Experience with CI/CD pipelines, Git-based version control, and automated deployment processes for data engineering workflows.
Demonstrated experience optimizing Snowflake performance, dbt models, SQL queries, and data warehouse costs.
Production experience working with and managing a Snowflake deployment.
Experience extracting information from SaaS systems such as Salesforce using ETL tools such as Fivetran.
Production experience using dbt Cloud or other source-driven transformation management tools.
Experience with Infrastructure as Code and Database Change Management tooling.
Experience with operational database management, including backup, recovery, tuning, data modeling, and scaling.
Demonstrated experience with Python, Java, Scala, or similar languages.
Must be self-motivated, focused, and highly committed to delivering value.
Must be a collaborative team player with strong communication skills, a willingness to share knowledge, and the ability to work effectively across cross-functional teams.

Nice To Haves

Experience preparing data for analysis in Tableau or other BI tools.
Experience with Tableau CRM Analytics Data Pipelines.
Experience with Sigma Computing.
Experience with Fivetran Activations.
Experience working in the Financial Services industry and familiarity with working in a SOC2 and PCI-DSS environment.

Responsibilities

Develop and manage data pipelines, data models, schemas, and data artifacts as a core contributor to the data squad.
Champion data engineering best practices, including data quality, version control, testing, and documentation.
Collaborate with Data, Software, and Platform Engineers to evolve and improve engineering practices, tools, and infrastructure.
Partner with data scientists, analysts, stakeholders, and cross-functional data teams to understand data requirements and translate them into technical solutions.
Stay up to date on emerging data engineering tools and technologies and help evaluate solutions to optimize the data ecosystem.
Collaborate with Platform Engineers to integrate and deploy data pipelines using shared CI/CD tools and Infrastructure as Code solutions, such as GitHub Actions and Terraform.
Monitor and troubleshoot data pipelines, proactively identifying and resolving issues to ensure data quality, reliability, and availability.
Develop and maintain documentation for data pipelines, data models, and engineering processes.