Principal, Data Analytics and Engineering

Key Responsibilities

• Hands-On Data Platform Engineering & Architecture
• Design, build, and directly contribute code to scalable data platforms on Google Cloud Platform (GCP).
• Lead hands-on engineering of Snowflake: schema design, performance tuning, resource optimization, and governance.
• Implement CI/CD pipelines, monitoring, logging, testing frameworks, and data quality automation.
• Serve as the primary technical expert for data reliability, platform performance, and architectural decision-making.
• Semantic Layer & Enterprise Reporting Enablement
• Architect and implement the AtScale semantic layer solutions, including aggregates, metrics, and performance optimizations.
• Provide hands-on technical support for Tableau data sources, extracts, and performance tuning.
• Establish patterns that ensure accurate, consistent, and trusted enterprise reporting.
• Integration Engineering & Data Pipelines
• Develop and optimize complex data flows using Workato, GCP services, and custom code.
• Build scalable ingestion frameworks for batch and streaming data.
• Troubleshoot and resolve pipeline issues at the system, code, and infrastructure levels.
• Customer Data Platform Engineering
• Provide hands-on ownership of the Amperity CDP, including identity resolution logic, profile stitching, segmentation workflows, and system integrations.
• Integrate and engineer data enrichment workflows with Bridg and other append platforms.
• Ensure data structures and pipelines enable advanced personalization and CRM activation.
• Enablement of Data Science & Advanced Analytics
• Engineer production-ready data products, feature sets, and ML-ready datasets.
• Build and operationalize model scoring pipelines in partnership with data science teams.
• Maintain documentation, lineage, and metadata standards for transparent analytics operations.
• Technical Leadership & Influence
• Set engineering standards through direct contribution and technical excellence.
• Conduct design reviews, propose architecture patterns, and drive platform evolution.
• Mentor engineers across data engineering, analytics engineering, and integrations—acting as the senior technical resource.
• Influence roadmaps and cross-functional decisions using hands-on insights and deep platform understanding.

Qualifications

• 10+ years in data and analytics engineering or multi-tier analytics platform architecture, with significant hands-on engineering experience.
• Expert with: GCP (GCS, Compute, storage optimization), Snowflake performance engineering, AtScale semantic modeling, Workato, Airflow, or similar orchestration tools, Amperity or similar CDPs, Advanced SQL, Python, and data modeling skills (dimensional, 3NF, Data Vault).
• Deep experience engineering large-scale retail or consumer datasets.
• Preferred: Experience designing ML feature stores and production-grade data science pipelines, Proven ability to lead architecture through hands-on contributions, Retail industry experience, Experience of integrating data platform with ERP and POS solutions in mid to large organizations, Excellent leadership, communication, and stakeholder management skills, Strong analytical, troubleshooting, and solution architecture skills, Excellent communication and stakeholder engagement abilities.