{"schemaVersion":"jobsearcher.job.v1","id":"6dfd20ac1043d16e20db8a7d","url":"https://jobsearcher.com/jobs/6dfd20ac1043d16e20db8a7d","canonicalUrl":"https://jobsearcher.com/jobs/6dfd20ac1043d16e20db8a7d","title":"Data Engineer","description":"Shift4 (NYSE: FOUR) is boldly redefining commerce by simplifying complex payments ecosystems across the world. As the leader in commerce-enabling technology, Shift4 powers billions of transactions annually for hundreds of thousands of businesses in virtually every industry. For more information, visit www.shift4.com.\n\nShift4 is looking for a Data Engineer to join the Shift4 data team and build the SQL and Python based jobs that turn raw payments data in Snowflake into reconciled, reliable datasets and reports. We currently have a library of containerized Python scripts that run SQL against Snowflake on a schedule. You will extend and improve this platform, making it more reliable, scalable and maintainable as we continue to evolve it.\n\nThis is a hands-on build role: you write the queries, wrap them in clean, testable Python, package them to run in containers, and make sure they produce correct numbers every time. Pipeline infrastructure (ingestion into Snowflake) is owned by the Data Platform team; you own what happens once the data is there, while embracing the Shift4 way.\n\nResponsibilities\n\nDesign, write, and maintain Python jobs that execute SQL over Snowflake to produce transaction, settlement, fee, and reconciliation datasets for internal and merchant-facing consumers\nWrite performant, readable SQL: window functions, CTEs, incremental logic, and semi-structured (JSON/VARIANT) handling on large transaction tables\nPackage jobs as Docker containers and run them on the existing scheduler; make every job idempotent, rerunnable, parameterized, and observable (logging, alerting on failure or bad output)\nImplement data-quality checks and reconciliation controls to ensure data is accurate and totals consistently align across sources and reporting periods.\nKeep an eye on Snowflake cost: warehouse sizing, clustering, query profiling, and avoiding wasteful scans\nWork with finance, operations, and product stakeholders to translate payments questions into correct, maintainable queries\nApply security controls appropriate for a payments environment: secrets handling, least-privilege access, PII masking, audit trails\nDocument the job library and contribute to code review, testing, and CI practices for the team\nProactively explore and apply AI tools to improve efficiency across the development lifecycle (query authoring, testing, documentation)\n\nQualifications\n\n1 - 3 years experience in data engineering, analytics engineering, or backend roles building SQL-heavy data workloads in production\nStrong SQL: window functions, CTEs, aggregation and joins over large tables, query tuning and reading execution plans\nHands-on Snowflake experience: warehouses and credit/cost awareness, stages and COPY INTO, streams and tasks, time travel, VARIANT/semi-structured data; Snowpark or the Snowflake Python connector\nSolid Python for data work: pandas or Polars, parameterized SQL, configuration and secrets management, structured logging, error handling, unit tests\nDocker fundamentals: building images, running scheduled batch jobs in containers, debugging container failures\nGit and basic CI/CD habits; comfortable with code review and writing testable, rerunnable jobs\nAttention to numerical correctness: you notice when totals do not tie out and you dig until you know why\n\nNice to have\n\nPayments domain knowledge: authorization, capture, settlement, refunds, chargebacks, interchange and scheme fees, merchant and acquirer data models, reconciliation\nOrchestration and transformation tooling: Airflow, Prefect, Dagster, dbt\nAWS from the developer side: S3, IAM, Secrets Manager, ECS/Fargate scheduled tasks\nData-quality frameworks (Great Expectations, dbt tests, Soda) and pipeline monitoring/alerting\nExperience in a PCI or otherwise regulated environment\nExposure to BI tools consuming the outputs (Sigma, Tableau, Power BI, Looker)\n\nWe are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.","company":"Shift4","rawCompany":"shift4","city":"Atlanta","state":"GA","isRemote":false,"isActive":false,"createdAt":"2026-10-09T11:27:50.588Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Engineer","description":"Shift4 (NYSE: FOUR) is boldly redefining commerce by simplifying complex payments ecosystems across the world. As the leader in commerce-enabling technology, Shift4 powers billions of transactions annually for hundreds of thousands of businesses in virtually every industry. For more information, visit www.shift4.com.\n\nShift4 is looking for a Data Engineer to join the Shift4 data team and build the SQL and Python based jobs that turn raw payments data in Snowflake into reconciled, reliable datasets and reports. We currently have a library of containerized Python scripts that run SQL against Snowflake on a schedule. You will extend and improve this platform, making it more reliable, scalable and maintainable as we continue to evolve it.\n\nThis is a hands-on build role: you write the queries, wrap them in clean, testable Python, package them to run in containers, and make sure they produce correct numbers every time. Pipeline infrastructure (ingestion into Snowflake) is owned by the Data Platform team; you own what happens once the data is there, while embracing the Shift4 way.\n\nResponsibilities\n\nDesign, write, and maintain Python jobs that execute SQL over Snowflake to produce transaction, settlement, fee, and reconciliation datasets for internal and merchant-facing consumers\nWrite performant, readable SQL: window functions, CTEs, incremental logic, and semi-structured (JSON/VARIANT) handling on large transaction tables\nPackage jobs as Docker containers and run them on the existing scheduler; make every job idempotent, rerunnable, parameterized, and observable (logging, alerting on failure or bad output)\nImplement data-quality checks and reconciliation controls to ensure data is accurate and totals consistently align across sources and reporting periods.\nKeep an eye on Snowflake cost: warehouse sizing, clustering, query profiling, and avoiding wasteful scans\nWork with finance, operations, and product stakeholders to translate payments questions into correct, maintainable queries\nApply security controls appropriate for a payments environment: secrets handling, least-privilege access, PII masking, audit trails\nDocument the job library and contribute to code review, testing, and CI practices for the team\nProactively explore and apply AI tools to improve efficiency across the development lifecycle (query authoring, testing, documentation)\n\nQualifications\n\n1 - 3 years experience in data engineering, analytics engineering, or backend roles building SQL-heavy data workloads in production\nStrong SQL: window functions, CTEs, aggregation and joins over large tables, query tuning and reading execution plans\nHands-on Snowflake experience: warehouses and credit/cost awareness, stages and COPY INTO, streams and tasks, time travel, VARIANT/semi-structured data; Snowpark or the Snowflake Python connector\nSolid Python for data work: pandas or Polars, parameterized SQL, configuration and secrets management, structured logging, error handling, unit tests\nDocker fundamentals: building images, running scheduled batch jobs in containers, debugging container failures\nGit and basic CI/CD habits; comfortable with code review and writing testable, rerunnable jobs\nAttention to numerical correctness: you notice when totals do not tie out and you dig until you know why\n\nNice to have\n\nPayments domain knowledge: authorization, capture, settlement, refunds, chargebacks, interchange and scheme fees, merchant and acquirer data models, reconciliation\nOrchestration and transformation tooling: Airflow, Prefect, Dagster, dbt\nAWS from the developer side: S3, IAM, Secrets Manager, ECS/Fargate scheduled tasks\nData-quality frameworks (Great Expectations, dbt tests, Soda) and pipeline monitoring/alerting\nExperience in a PCI or otherwise regulated environment\nExposure to BI tools consuming the outputs (Sigma, Tableau, Power BI, Looker)\n\nWe are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.","datePosted":"2026-10-09T11:27:50.588Z","dateModified":"2026-10-09T11:27:50.588Z","hiringOrganization":{"@type":"Organization","name":"Shift4","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Atlanta","addressRegion":"GA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6dfd20ac1043d16e20db8a7d"},"url":"https://jobsearcher.com/jobs/6dfd20ac1043d16e20db8a7d"}}