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%