Senior Data Engineer building trusted Snowflake data products for Granum’s landscaper and arborist software ecosystem. Operating governed pipelines that power analytics, AI, and operational decisions.
About the role
- Director of Data Engineering leading end-to-end data organization at 73 Strings. Building scalable data platforms and driving AI-led innovation in a FinTech powerhouse.
Responsibilities
- Define and execute the company’s multi-year data strategy, roadmap, and target data architecture.
- Build, scale, and lead high-performing teams across data science, ML engineering, data engineering, analytics, and data governance.
- Establish organisation-wide standards for data quality, consistency, access, and governance.
- Work with product and engineering to enable data driven product decisions and build end-to-end data platform
- Architect scalable, secure, and high-performance data platforms and pipelines.
- Oversee ingestion, modelling, transformation, and orchestration of structured and unstructured data.
- Ensure modern data stack best practices (e.g., warehouse/lakehouse, orchestration, feature stores, catalogues, ML pipelines).
- Optimize and transform data for low latency delivery to client facing applications (web, Excel etc.).
- Lead development, deployment, monitoring, and lifecycle management of ML models.
- Partner with product teams to embed ML features into customer-facing products.
- Enable AI innovation with modern data and machine learning infrastructure.
- Oversee creation of dashboards, metrics frameworks, and analytical tools that empower teams across the business.
- Ensure analytics output is accurate, trustworthy, and tied to business outcomes.
- Partner with finance, sales, and leadership on forecasting, performance analysis, and strategic planning.
- Implement data governance practices including access control, metadata management, and lineage.
- Ensure compliance with relevant regulatory frameworks (GDPR, CCPA, SOC 2, etc.).
- Drive data quality programs with automated monitoring and remediation processes.
Requirements
- Proven experience building, leading, and scaling multi-disciplinary data teams of 10+ members across data engineering, analytics, and machine learning functions.
- Deep expertise in modern data architecture, cloud data platforms, and scalable pipelines.
- Proven leadership experience managing multi-disciplinary data teams.
- Demonstrated success within innovative, product-led technology companies, ideally operating in data-intensive or regulated domains.
- Experience implementing data governance, data quality frameworks, and operational data processes.
- Strong business acumen with ability to translate complex data into strategic, actionable insights.
- Excellent communication skills and ability to influence senior leadership.
Benefits
- Culture of innovation and collaboration
- Empowerment and continuous learning for team members
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Degree requirement
No Education Requirement
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