Here’s How You Make an Impact:
- AI and Data Platform Evolution: Lead the development and optimization of a secure, scalable AI and data platform that supports Wave’s growth, managing data ingestion, our data lake, and AI/ML infrastructure on AWS.
- DataOps Implementation: Lead the adoption and integration of DataOps practices to automate and optimize data workflows, ensuring data quality, lineage, and availability across the platform. Align DataOps with AI/ML initiatives to support continuous improvement and scalability.
- Architectural Oversight: Continuously evolve Wave’s data, DataOps and AI/ML architecture by reviewing, recommending, and implementing changes, integrating new tools, and ensuring thorough documentation.
- AI Model Management: Oversee the full lifecycle of ML models, including training, deployment, monitoring, and optimization, while managing our portfolio of generative AI applications.
- Collaboration and Partnership: Collaborate closely with internal technology partners (Compliance, Engineering, IT, Security) and external partners like AWS to align initiatives with organizational goals.
- Data and AI Integrity: Ensure data and AI model quality, lineage, and integrity across the platform through robust systems and processes.
- Governance and Compliance: Implement and maintain governance processes that address stakeholder needs, emphasizing security, transparency, and efficiency.
- Project Management: Lead the team in scoping, prioritizing, and executing data and AI projects, setting clear goals, tracking progress, and removing obstacles.
- Mentorship and Best Practices: Mentor the team in best practices for data engineering and AI/ML operations, conducting regular 1-on-1s, setting goals, and holding the team accountable.
- Stakeholder Management: Represent the team with confidence and transparency, managing relationships across all data and AI functions.
You Thrive Here By Possessing the Following:
- 7+ years in data and AI/ML, including 5+ years in data platform architecture (data pipelines, governance, and architecture).
- 5+ years managing teams of data and/or AI/ML professionals, with a track record of leading cross-functional teams and complex projects.
- Advanced SQL skills for querying large, complex datasets.
- Proficient in Python, with experience in data frame-based libraries (Pandas, Dask, Polars) and AI/ML libraries (TensorFlow, PyTorch, Scikit-learn).
- Strong understanding of DataOps practices, including automated data pipelines, data quality monitoring, and CI/CD for data.
- Experienced with AWS services (S3, Redshift, RDS, DynamoDB, SageMaker).
- Strong understanding of distributed computing (Apache Spark) and hands-on experience with AWS Glue.
- Solid grasp of MLOps practices, including CI/CD for AI/ML and maintaining evolving data and AI system architectures.
- Familiar with data pipeline tools like Kafka, dbtCloud, Terraform, and Kubernetes.
- Expertise in DevOps and Infrastructure as Code (IaC) with Terraform HCL.
- Proven ability to collaborate with internal partners (Compliance, Engineering, IT, Security) and external partners (AWS) to align initiatives with organizational goals.
- Experienced in Agile team leadership using Scrum, with skills in sprint performance reporting, backlog grooming, and alignment with OKRs and KPIs.
- Success in setting, meeting, and exceeding quarterly OKRs, with consistent KPI reporting.
- Committed to mentoring through regular 1-on-1s, setting goals, and providing constructive feedback.
- Effective communicator, able to convey complex technical matters visually and verbally.
- Proactive, autonomous project manager with a strong ability to partner with business and engineering stakeholders from ideation to execution.
Nice to Have:
- Generative AI Expertise: Expertise in generative AI, including understanding key techniques and applications.
- RAG, LLM, and Agentic Patterns: Familiarity with Retrieval-Augmented Generation (RAG), Large Language Model (LLM) fine-tuning, and agentic GenAI patterns.
- AWS Bedrock and LangChain: Experience deploying and managing generative AI with AWS Bedrock, LangChain, or similar platforms.
- Data Privacy and Compliance: Experience implementing data privacy and compliance solutions, especially with AI Management Systems like ISO 42001.
- Third-Party Tools: Experience with Stitch, Segment, and integrating third-party data tools into platforms.
- Cross-Industry Experience: Knowledge or experience in Payroll, Banking, Fintech, Payments Processing, or Accounting industries where data and AI/ML are critical.
- Data Product Management: Experience managing data product lifecycles, including versioning, monitoring, and continuous improvement.
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