Senior Data Lead Engineer

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.
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