Senior Manager. Data Engineering 360training is seeking a seasoned Data Engineering Leader with expertise in Snowflake, Azure, and Modern Data Architecture. The chosen leader will guide our Data Engineering team in developing and maintaining our Enterprise Data Platform and core data/application pipelines. They will also own the go-forward data strategy, partnering with Development, Analytics, and business teams to drive insights and deliver scalable, cost-efficient data solutions. Our ideal candidate is a rising star in Data Engineering leadership, with a proven ability to build high-performance teams, deliver innovative solutions, and bridge business needs with technical execution. This role is critical in shaping our next-generation data platform, with emphasis on data quality, speed of execution, and cost optimization. Key Responsibilities: Leadership & Team Development - Lead, manage, and mentor a team of data engineers to deliver high-quality data solutions.
- Recruit, onboard, and grow top engineering talent (both local and global), leveraging offshore resources where appropriate.
- Establish clear team goals, KPIs, and expectations, and ensure timely delivery of strategic initiatives.
- Foster a culture of continuous learning, innovation, and operational excellence.
Data Platform Strategy & Execution - Drive the design and implementation of modern cloud-based data architectures, with a strong emphasis on Snowflake as the enterprise data warehouse and Azure-based solutions.
- Lead adoption of the Snowflake ecosystem (Snowpipe, Streams, Tasks, Data Sharing, DBT/other orchestration frameworks) to optimize data pipelines.
- Partner with Data Analytics to ensure data models and structures support business intelligence and reporting needs.
- Oversee performance tuning, cost optimization, and scalability planning across Snowflake and Azure.
- Ensure enterprise-grade data quality, consistency, and governance across all platforms.
Data Governance & Compliance - Partner with stakeholders to ensure robust data governance policies are implemented, covering data quality, security, lineage, and access control.
- Maintain compliance with regulatory and certification frameworks including SOC 2, ISO 27001, and PCI DSS.
- Support audit processes by ensuring documentation and controls are consistently applied.
Cost & Resource Management - Proactively monitor and manage Snowflake credits and Azure resource usage to align with budget targets.
- Forecast and control data platform spend in partnership with Finance and Procurement.
- Balance performance and cost efficiency when designing data solutions.
Integration & Real-Time Data - Expand capabilities for both batch and streaming pipelines (e.g., Kafka, Event Hub, or equivalent).
- Ensure the architecture supports analytical, near real-time, and operational use cases.
- Partner with application and microservices teams to build data flows supporting modern event-driven architectures.
Collaboration & Delivery - Partner with cross-functional teams (Product, QA, DevOps, Data Analytics) to deliver projects on time and with exceptional quality.
- Actively participate in architectural reviews, providing technical guidance and ensuring best practices.
- Promote a DevOps culture in data engineering, including CI/CD, automation, monitoring, and infrastructure as code.
- Ensure SLAs for production data services are met while minimizing costs and downtime.
Stakeholder Engagement & Roadmap - Act as the primary point of contact for business stakeholders regarding data engineering priorities and initiatives.
- Define and communicate the data engineering roadmap, ensuring alignment with corporate objectives and IT strategy.
- Regularly report progress, risks, and recommendations to senior leadership.
Innovation & Continuous Improvement - Stay ahead of emerging data engineering tools and practices, particularly in the Snowflake and Azure ecosystems.
- Identify and implement new strategies for improving speed, reliability, and scalability of data pipelines.
- Champion a forward-looking data strategy aligned to business goals.
Required Skills: - 5+ years of Data Engineering leadership experience, with a track record of building and scaling data teams.
- Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
- Hands-on experience with Snowflake (warehouse design, performance tuning, pipeline orchestration, cost optimization).
- Proven expertise in Azure cloud data services (Azure Data Factory, Azure SQL, Power BI, Synapse, etc.).
- Strong understanding of data engineering productivity toolchains (CI/CD, DevOps, GitHub, Jira, Confluence, Agile Scrum/Kanban).
- Experience designing and implementing scalable architectures for analytics and operational workloads.
- Comfortable collaborating directly with business and analytics teams to bridge requirements with technical execution.
- Excellent problem-solving, troubleshooting, and communication skills.
- Passion for innovation, learning, and driving modern data practices.
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