Back
Mid-level metro data engineering role with broad skillset and popular title yields high candidate competition.
Core data engineering skills transfer across industries, but entity-resolution and historical-archive experience add moderate domain specificity.
Explicit 5+ years requirement plus many mandatory technical skills and domain experience makes shortlisting strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own the design and evolution of the canonical data model spanning multiple sports and diverse data sources to support analytics, modelling, and engineering teams.
Build, maintain, and ensure reliability, scalability, and accuracy of data warehouses, data marts, and data pipelines processing large historical and live datasets, handling entity resolution, deduplication, and reconciliation.
Set technical direction, standardize data engineering practices including schema versioning and data governance across a growing data team.
5+ years of experience in Data Engineering with ownership of data models and platforms beyond initial use cases.
Strong expertise in dimensional modelling, data warehouse design, expert SQL and Python skills.
Experience handling data quality challenges like entity resolution, deduplication, and schema management across complex multi-source datasets.
Bachelor's degree in Computer Science, Engineering, Information Technology, or related field; experience with modern data warehousing and cloud infrastructure technologies.
Experienced in managing complex data architectures that serve analytics and modelling workloads with evolving requirements, showing strong operational and strategic data platform ownership.
Comfortable working independently in small teams making sound technical decisions and collaborating closely with product and modelling teams to translate requirements into data structures.
Preferably has experience with sports data or large-scale historical datasets and familiarity with data quality, governance, and schema versioning in production environments.