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Tier‑1 brand, metro role, and broad generalist data/ML requirements increase applicant competition.
Requires Databricks, PySpark, lakehouse and production forecasting expertise, limiting cross-industry transferability.
Explicit 7+ years and mandatory Databricks/PySpark, production ML and cloud skills raise filter strictness.
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Design, architect, and own a modern Databricks lakehouse platform integrating data engineering and applied AI/ML to deliver scalable production-grade data products.
Lead development and optimization of batch and event-driven ELT/ETL pipelines with embedded validation, CI/CD, monitoring, and observability.
Drive advanced analytics including time-series forecasting, anomaly detection, classification models, and automated data quality/schema monitoring, directly impacting operational efficiency and financial outcomes.
7+ years experience in data engineering, Machine Learning, and production-grade data pipelines/platforms in cloud environments.
Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or related field; Master’s preferred.
Proven hands-on experience with Databricks/Spark, lakehouse architectures, and cloud data engineering (Azure/AWS/GCP) or Databricks certifications.
Expertise in SQL, Python, PySpark, statistical modeling (time-series forecasting, anomaly detection), and data quality and schema monitoring automation.
Experienced leader comfortable setting engineering standards, leading design reviews, mentoring engineers/data scientists, and managing stakeholder expectations in complex data environments.
Strong cross-functional collaborator skilled at integrating data/ML back-end services with web front ends and building event-driven microservices and API architectures.
Technical operator with advanced expertise in scalable predictive analytics, MLOps including CI/CD and monitoring, and ability to deliver measurable impact through data-driven forecasting, anomaly detection, and risk scoring.