Data & Analytics Engineer
Description Summary: The Data Analytics Engineer will be a foundational technical contributor within ADT's Data & AI organization. This role sits at the intersection of data engineering and data analysis, responsible for designing, building, and optimizing clean, reliable, and well-modeled datasets that power enterprise analytics and data science models. Operating within a Google Cloud Platform (GCP) ecosystem, this individual will transform high-dimensional enterprise master data and models, spanning customer transactions, web analytics, and smart home IoT telemetry, into production-grade, structured data and metric semantic layers. The ideal candidate treats data as code, applying software engineering best practices to simplify consumption for reporting and analytics. The ideal candidate builds on our existing enterprise master data models to drive data quality and democratization across ADT. Duties and Responsibilities: - Enterprise Semantic Layer: Build and maintain a scalable, reliable, and clean semantic layer using Google Cloud applications like BigQuery, Dataform, and LookML with Tableau to transform and standardize disparate business-owned reporting objects. - Engineer AI-Ready Data: Standardize metadata, documentation, and explicit metric definitions to optimize data for Gemini Enterprise. Ensure that our generative AI models can accurately query multi-modal data without hallucinating or misinterpreting business definitions. - Bridge Unstructured and Structured Data: Collaborate on integrating our unstructured data lake (GCS) with our structured enterprise data platform in BigQuery to create unified logical views. - Promote Self-Service and Reduce Reporting Friction: Empower business partners to confidently answer their own basic ‘reporting’ questions through an airtight semantic layer, freeing up analytical mindshare for deep statistical and prescriptive analytics. - Cross-Functional Data Collaboration: Partner closely with enterprise data engineers to build on ‘single source of truth’ data and collaborate with enterprise machine learning engineers to ensure feature definitions align between analytic and MLOps platforms. - Self-Service Enablement: Partner closely with data scientists, analysts, and business intelligence teams to understand their requirements and build intuitive reporting tables that accelerate insights and dashboard development (e.g., in Tableau, Data Studio). - Data Quality & Testing: Implement rigorous automated testing (using Dataform, BigQuery, or similar frameworks) and continuous monitoring to ensure data integrity, performance, and accuracy across the reporting layer. - Code Governance & CI/CD: Apply software engineering best practices to the analytics workflow, including version control (Bitbucket), rigorous code reviews, documentation, and continuous integration/continuous deployment (CI/CD). - Performance Optimization Collaboration with DataOps: Continually audit and optimize BigQuery SQL queries, table partitioning, clustering strategies, and pipeline runtimes to manage GCP compute costs and maximize speed. - Data Security & Privacy: Ensure data models comply with data privacy regulations, balancing masking and securing sensitive customer information while enabling powerful downstream reporting. Minimum Qualifications: - Education: Bachelor’s degree (or equivalent experience) in Computer Science, Information Systems, Statistics, Mathematics, Data Analytics, or a related highly quantitative field. - Experience: 3+ years of professional experience in data engineering, analytics engineering, or high-level data analysis with an emphasis on production data pipeline development. - Technical Proficiency: - Advanced SQL Mastery: Expert-level ability to write, optimize, and audit complex analytical SQL queries inside modern cloud data warehouses (Google Cloud). - Proficiency in Python for data manipulation and scripting (e.g., Pandas, Airflow operators). - Direct hands-on experience working in cloud data warehouses, specifically Google BigQuery and Google Cloud Storage (GCS). - Software Practices: Strong familiarity with Bitbucket workflows and version control practices, and automated data validation tests. - Data Literacy and Translation: Strong ability to sit between deep technical engineering teams and non-technical business leaders to extract, define, and standardize metric logic. - Ambiguity Management: Exceptional problem-solving skills with the versatility to take vague business definitions and map them to reliable, structured technical data tables. Required Licensing or Certifications: - Preferred: Google Cloud Certified Professional Data Engineer Communication Skills: - Writing, Talking/Hearing on the phone (Continually=67-100% of workday) Environment Requirements: - Remote/Home office (Continually=67-100% of the workday) - Travel: Occasionally, less than 25%