Data & Analytics Engineer

Role Overview

The Data Analytics Engineer will be a foundational technical contributor within ADT’s Data & AI organization, responsible for designing, building, and optimizing clean, reliable, and well-modeled datasets that power enterprise analytics and data science models.

What You Will Do

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.

Why It Might Be a Fit

The ideal candidate treats data as code, applying software engineering best practices to simplify consumption for reporting and analytics, and has strong ability to sit between deep technical engineering teams and non-technical business leaders to extract, define, and standardize metric logic.

Requirements

• Bachelor’s degree (or equivalent experience) in Computer Science, Information Systems, Statistics, Mathematics, Data Analytics, or a related highly quantitative field.
• 3+ years of professional experience in data engineering, analytics engineering, or high-level data analysis with an emphasis on production data pipeline development.
• 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.

Benefits

• Dental insurance
• Vision insurance
• Medical insurance
• 401(k) or retirement plan
• Paid time off (vacation, sick leave)
• Health savings account (HSA)
• Flexible work arrangements (telecommute)
• Professional development opportunities
• Employee assistance program (EAP)
• Life insurance
• Disability insurance
• Stock options
• Bonus/commission
• Paid holidays

Tagged as: BigQuery, Data Analysis, Looker, Pipeline Management, Python