Overview
BEST is modernizing our data capabilities to accelerate product innovation, data-driven decision making, and AI-enabled experiences. Today the organization operates with traditional data engineering practices; we are hiring a transformation leader who will modernize the platform, build production-grade ML/AI capabilities, and embed responsible AI across the business.We’re hiring a strategic, hands-on
Sr. Manager, Data Services
to own and scale BEST’s data platform and teams—leading engineers and managers across ingestion, transformation, platform services, data products/APIs, governance, and production ML/AI. You’ll be accountable for MLOps, model serving, and the people/processes to safely operationalize AI at scale, transforming a traditional data-engineering function into an AI-first, product-oriented organization. This role requires deep technical expertise in data platforms, MLOps and LLM/AI patterns, plus proven people leadership and cross-functional influence.
Responsibilities
- 12+ years’ experience in data engineering, data platform, analytics, or ML engineering roles, with at least 3+ years in a people-/team-management role.
- Hands-on experience building and operating large-scale data platforms (data lakes/warehouses, Spark/Databricks, Snowflake/Redshift/BigQuery, streaming with Kafka/Kinesis).
- Demonstrated track record of taking ML/AI projects from POC to production. Practical experience with MLOps tooling and pipelines (e.g., MLflow, Kubeflow, SageMaker, or equivalent).
- Deep familiarity with model serving and inference architectures (real-time and batch), model monitoring, automated retraining, and drift detection.
- Strong engineering skills: Python / SQL (and/or Scala/Java), data pipeline frameworks, and versioned data/model workflows.
- Experience with LLMs and modern AI application patterns: prompt engineering, retrieval-augmented generation (RAG), knowledge graphs, LLM safety/guardrails, and LLMOps frameworks (e.g., LangChain, LlamaIndex).
- Cloud platform experience (AWS / Azure / GCP) and experience designing cost-efficient, production-grade ML inference at scale.
- Experience with data governance, cataloging (Amundsen/Atlas/Data Catalog), lineage and privacy compliance (GDPR/CCPA) in a production environment.
- Excellent people and cross-functional leadership skills, with strong communication, stakeholder influence, and program management ability.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field. Advanced degree (MS/PhD) in a quantitative or engineering discipline preferred.
Qualifications
Key responsibilities
- Strategy & Roadmap — Define and operationalize a 12–24 month roadmap for Data Services that supports company data and AI goals (data APIs, platform, governance, ML pipelines, model serving and monitoring).
- Team Leadership — Hire, mentor and grow a high-performing team (data engineers, platform engineers, ML engineers, SREs). Build leadership bench and establish clear career paths.
- Data Platform Ownership — Own the design, delivery, and run-book for data platforms (data lake/warehouse, streaming, ingestion, processing, metadata/catalog, lineage and observability).
- ML/AI Enablement — Partner with AI/ML teams to build repeatable ML pipelines and production model serving (batch & real-time). Lead MLOps adoption: model training, deployment, CI/CD, drift detection, and model governance.
- AI Productionization — Drive production-grade implementations of AI use-cases (including large language models, retrieval-augmented generation, knowledge graphs, agentic/automated workflows). Ensure safe, scalable, and cost-efficient model serving and inference.
- Data Products & APIs — Deliver production data products and APIs consumed by analytics, product teams, and AI systems. Enable self-service data and ML capabilities for downstream teams.
- Quality, Observability & Governance — Implement enterprise-grade data quality, lineage, cataloging, access controls, and privacy/compliance practices. Partner with security and legal on policy for AI model usage and data privacy.
- Stakeholder Management — Work cross-functionally with product, engineering, marketing, sales, and COE to translate business needs into data and AI programs, prioritizing high-impact work and measuring outcomes.
- Metrics & Outcomes — Define and report KPIs for platform reliability, time-to-insight, model accuracy/performance, cost, and adoption.
