> Markdown version of [/jobs/ext/4202664-senior-data-engineer](https://www.wearedevelopers.com/jobs/ext/4202664-senior-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Reach Plc - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $150,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Java (Programming Language), Airflow, Software as a Service, Cloud Computing, Databases, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Stores, Data Warehousing, Github, Python (Programming Language), PostgreSQL, NoSQL, Operational Databases, PCI Data Security Standards, Performance Tuning, Standard Sql, DataOps, Salesforce.Com, Scala (Programming Language), SQL Databases, Tableau (Software), Snowflake, Git, Amazon Relational Database Service, Deployment Automation, Terraform, Software Version Control, Data Pipelines - **Published:** October 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=cd9aac6c82bcfc18 ## About the Role * 6+ years of hands-on experience in data engineering, working with dbt (Core or Cloud), Snowflake, Fivetran, Amazon RDS, PostgreSQL, etc * 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 a production environment. * 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 like Salesforce using ETL tools like 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, 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. ## Description As a Senior Data Engineer at Reach, you'll work alongside our existing Data Engineering team on our data platform modernization effort. You'll be a key contributor to our broader data practice, building and maintaining pipelines, data models, and transformations as we modernize Reach's data stack. You'll help establish scalable, reliable, and consistent data engineering practices across the organization, ushering in a new era of cohesive data design and management within the broader Engineering organization. What you'll do * Develop and manage data pipelines, data models, schemas, and data artifacts as a core contributor to our 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 existing engineering practices, tools, and infrastructure. * Partner with data scientists, analysts, stakeholders, and cross-functional data teams across the organization to understand data requirements and translate them into technical solutions. * Stay up to date on emerging data engineering tools and technologies, and help evaluate new solutions to optimize our data ecosystem. * Collaborate with Platform Engineers to ensure seamless integration and deployment of 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.