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Software Engineer III – Data

Vaga não está no LinkedIn

🕒 Setembro 7

🇬🇧 Reino Unido – Remoto (RU)

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

🗣️🇺🇸🇬🇧 Inglês obrigatório

De onde vem esta vaga

Publicada na página de carreiras da Fanatics Betting & Gaming, não em um site de vagas

Vista pela primeira vez pelo Remote Rocketship Setembro 7

Ainda aberta no site da Fanatics Betting & Gaming. Verificada 12 horas atrás.

Logo of Fanatics Betting & Gaming

Fanatics Betting & Gaming

- funcionários

🎲 Jogos de Azar

⚽ Esportes

👥 B2C

Gambling • Sports • B2C

A Fanatics Betting & Gaming é uma operadora americana voltada ao consumidor de apostas esportivas e cassino online (com as marcas Fanatics Sports & Casino / Fanatics Sportsbook / Fanatics Casino), que oferece apostas esportivas, jogos de cassino online, negociação de contratos de eventos (Fanatics Markets) e uma moeda de fidelidade integrada chamada FanCash. A empresa disponibiliza aplicativos móveis e experiências na web, com operação disponível, estado a estado, em diversas jurisdições dos Estados Unidos, além de ofertas promocionais, parcerias com equipes esportivas e ferramentas e informações sobre jogo responsável. A Morton St. Trading Investments, LLC é responsável pela operação de negociação de contratos de eventos e está registrada na CFTC.

Descrição

• Implement new ingestion sources end-to-end against a senior engineer's design, including connector code, DAG, schema, monitoring, and catalog registration • Extend the team's existing ingestion framework where a new source needs an unsupported pattern • Investigate pipeline failures independently, recognize known issue classes, and ship documented fixes • Identify downstream consumers and risks before shipping new pipelines, and flag gaps during specification review • Write clear handovers when escalating unresolved issues • Review peers' pipeline pull requests and catch non-obvious issues such as missing idempotency checks and race conditions • Turn ambiguous data-quality questions into structured investigations by tracing data lineage from source to warehouse • Surface concerns in specification reviews as specific, well-reasoned questions • Support data security and governance work, including PII masking and access controls • Contribute to data delivery work, including reverse ETL integrations • Build relationships with internal stakeholders and help scope and clarify requirements • Mentor DE2s on significant projects and help them apply team conventions

🎯 Requisitos

• 3–5 years of professional software or data engineering experience • Strong SQL and Python skills, with solid experience building and operating production data pipelines • Comfort investigating and root-causing pipeline issues independently before escalating • Experience with workflow orchestration tools (Airflow or similar) and a cloud data warehouse/lakehouse (Snowflake, Databricks, or similar) • Solid understanding of data pipeline concepts: idempotency, schema evolution, backfills, and data quality/testing • Experience giving substantive code review feedback, not just style or formatting comments • Strong communication skills — can write a clear technical handover, ask sharp questions in spec review, and explain a data lineage investigation to a non-technical stakeholder • A track record of taking ownership of known-class problems end-to-end rather than needing step-by-step direction • Experience extending or building reusable pipeline frameworks/templates • Exposure to reverse ETL tools or patterns, PII masking, or data access governance (RBAC) • Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting • Some experience mentoring or informally supporting more junior engineers • Background in gaming, betting, e-commerce, or another regulated/high-compliance industry

🏖️ Benefícios

• Real ownership over known-class problems, with senior/staff support available for the genuinely novel ones • Work on high-visibility, high-trust systems that the business depends on • A culture built around clear tenets: standardize before you scale, own the outcome (not just the ticket), and clarity over complexity • Clear growth path into Senior Data Engineer, with room to start mentoring and shaping team practices along the way

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