Software Engineer III - Data - FMX

Role overview A mid-level data engineer is needed to build and operate the data pipelines that underpin a large consumer sports platform. The role sits within a central data engineering function and focuses on shipping reliable ingestion pipelines, root-causing failures, and contributing to a healthy code review culture. Senior engineers design the work; this hire implements against that design, resolves known-class issues independently, and escalates genuinely novel problems with high-quality context.

Responsibilities

- Implement new ingestion sources end to end against an existing framework, including connector code, orchestration DAGs, schemas, monitoring, and catalog registration. - Investigate and resolve pipeline failures independently, applying known fixes and avoiding unnecessary escalation. - Trace data lineage from source to warehouse when stakeholders raise data-quality questions and report findings back clearly. - Contribute substantive code review on peers' pipeline pull requests, catching issues such as missing idempotency checks or race conditions. - Support data security and governance work, including PII masking, access controls, and reverse ETL integrations. - Mentor less experienced engineers on first significant projects and help them apply team conventions.

Requirements

- Three to five years of professional software or data engineering experience. - Strong SQL and Python skills, with hands-on experience building and operating production data pipelines. - Experience with workflow orchestration tools such as Airflow and a cloud data warehouse or lakehouse such as Snowflake or Databricks. - Solid understanding of idempotency, schema evolution, backfills, and data quality or testing practices. - Ability to write clear technical handovers and explain lineage investigations to non-technical stakeholders. - Track record of owning known-class problems end to end without needing step-by-step direction.

Nice to have

- Experience extending or building reusable pipeline frameworks or templates. - Exposure to reverse ETL tooling, PII masking, or role-based access governance. - Familiarity with observability or monitoring tooling such as Datadog for pipeline health alerts. - Background in gaming, betting, e-commerce, or another regulated or high-compliance industry.

Benefits and work setup

- Ownership of known-class problems with senior support available for novel issues, plus a defined growth path toward senior data engineering with mentoring responsibilities.