Role Summary As a Senior Data Lead Engineer , you will lead the evolution of cloud-based data, AI, and BI platforms, owning the data lakehouse architecture and enabling high-impact analytics and AI use cases in a regulated, large-scale environment. What You’ll Actually Do Own and evolve the data & AI roadmap ensuring scalability, security, and cost efficiency. Design and maintain the enterprise lakehouse architecture. Deliver high-quality, trusted datasets for analytics, BI, and AI use cases. Act as a technical leader and mentor within the data organization. Must-Have Requirements (Real Essentials) These are the non-negotiables—everything else is a plus. Core Experience 5+ years in Data Engineering / Data Platforms / Advanced Analytics (enterprise or regulated environments). Proven experience designing and owning cloud data platforms and lakehouse architectures (preferably AWS). Hands‑on experience with Databricks or EMR (Spark) for large‑scale data processing. Strong background building data ingestion & ETL pipelines, including CDC and event‑driven architectures. Experience enabling AI/ML use cases in production (end‑to‑end, not just POCs). Architecture & Engineering Strong knowledge of AWS data services (S3, Glue, EMR, Lake Formation). Solid SQL and Python for data processing and automation. Experience with hybrid architectures (on‑prem + cloud) and enterprise data integration. Practical understanding of data governance, data quality, lineage, and security guardrails. Data as a Product Experience working with business and BI teams to define KPIs and deliver consumable datasets. Ability to design well‑modeled datasets and semantic layers optimized for analytics and reporting. Familiarity with data mesh principles (domain ownership, data‑as‑product mindset). Leadership Proven ability to lead technically, mentor engineers, and influence stakeholders without direct authority. Strong communication skills with both technical and non-technical audiences. Nice to Have (We Won’t Filter You Out for This) AWS or Databricks certifications. Experience with ML workflows (feature engineering, training, deployment, monitoring). Exposure to LLMs (RAG, fine‑tuning, prompt engineering, guardrails). Experience with CI/CD, orchestration tools, or Infrastructure as Code. Familiarity with BI tools (QuickSight, Power BI, Qlik) from a data provider perspective. 100 % Remoto #J-18808-Ljbffr