Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL processes.
Responsibilities
Expected to be an SME, collaborate and manage the team to perform.
Lead design and development of complex PLX-based dashboards, workflows, data processes, analytical tools, and automation solutions.
Translate functional and analytical requirements into scalable technical designs and implementation plans.
Develop advanced SQL logic for large datasets, including transformation, validation, optimization, metric calculation, and exception handling.
Build or guide Python-based automation, data validation, analytical processing, and repeatable workflows in Colab.
Senior Data Engineer responsible for building and supporting data pipelines, both batch and real-time, and leading a team of data engineers.
Compensation
The base compensation range for this role in the posted location is $83,951 to $135,000.
Responsibilities
Demonstrate deep knowledge of the data engineering domain to build and support non-interactive (batch, distributed) & real-time, highly available data, data pipeline, and technology capabilities
Build fault-tolerant, self-healing, adaptive, and highly accurate data computational pipelines
Provide consultation and lead the implementation of complex programs
Develop and maintain documentation relating to all assigned systems and projects
Tune queries running over billions of rows of data running in a distributed query engine
Participate in design, development, testing, and verification of complex electrical, mechanical and/or software systems and components using a systems engineering approach.
Compensation
The pay range for this position is: Level 4 ($114,360 - $234,336) / Level 5 ($137,076 - $281,196).
Responsibilities
Apply Systems Engineering principles and techniques to support and carry out the engineering design process, including requirements development, decomposition, verification, and traceability across the full project/system lifecycle.
Develop, implement, and integrate systems engineering principles throughout the project/system lifecycle. including systems and organizational integration, road mapping, and testing.
Use digital engineering tools — including requirements management platforms (e.g., DOORS), product lifecycle management (PLM) tools (e.g., PTC Windchill), and collaborative review platforms (e.g., CoLab) — to implement systems engineering on all assigned projects.
Create, revise, and manage configuration baselines for System Design Descriptions (SDDs), Functional and Operational Requirements (F&OR), Concepts of Operations (ConOps), and Code of Record (COR) documents.
Reconcile discrepancies between requirements management databases (e.g., DOORS), document control systems (e.g., EDMS), and source documents; maintain synchronization and traceability across artifacts.
The Data Scientist, Marketing Analytics serves as a core analytical authority on ADT’s marketing efforts, translating complex customer, campaign, and media data into high-value, predictive, and actionable insights.
Responsibilities
Predictive Growth Modeling: Apply advanced statistical methods, machine learning (e.g., classification, survival analysis), and clustering to predict customer lifetime value (LTV), model acquisition propensity, and identify customer churn risk.
Attribution & Mix Modeling: Support the development of Marketing Mix Modeling (MMM) and multi-touch attribution (MTA) frameworks to help business leaders understand and act on marketing efficiency issues.
Campaign Experimentation: Design and execute evidence-based research and scientific hypothesis testing (A/B and multivariate testing) around digital marketing campaigns, creative assets, and landing page personalization.
Executive Data Storytelling: Translate highly complex statistical findings into clear, beautiful, and intuitive visualizations and compelling narratives that drive strategy, action, and decision-making at executive levels.
Data Engineering & Pipelines: Collaborate with data platform and engineering teams to design, implement, and maintain robust data pipelines that integrate ad-platform APIs, web analytics, and offline sales data into a continuous, single-source-of-truth.
Requirements
Bachelor’s degree (or equivalent experience) in Analytics, Computer Science, Statistics, Mathematics, Economics, or a related highly quantitative field.
Software Engineer, App Ads Bidding and Optimization
Develop and optimize advertising technology for Google Ads, focusing on bidding algorithms, machine learning models, and data analysis to improve campaign performance.
Improve the accuracy and stability of the UAC bidder with statistical analysis and new ML/optimization algorithms
Improve/modernize bidding infrastructure and design and implement bidding algorithms for faster cold start for new campaigns
Debug inaccuracies in bidding infrastructure and suggest improvements and launch with design, implementation, experimentation and analysis
Incorporate and engineer input features from ad creatives, publisher pages/content, as well as users’ personalized ad history to improve bidding and modeling accuracy
Improve the efficiency and reliability of ML models by designing novel model architectures and adopting robust development and release processes
Requirements
Bachelor's degree in Computer Science or related technical field
Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL processes to migrate and deploy data across systems.
Responsibilities
Expected to be an SME, collaborate and manage the team to perform.
Lead design and development of complex PLX-based dashboards, workflows, data processes, analytical tools, and automation solutions.
Translate functional and analytical requirements into scalable technical designs and implementation plans.
Develop advanced SQL logic for large datasets, including transformation, validation, optimization, metric calculation, and exception handling.
Build or guide Python-based automation, data validation, analytical processing, and repeatable workflows in Colab.
Senior Data EngineerCapgemini · Vancouver, Canada
Mechanical Engineer Level 2/3Idaho National Laboratory · Idaho Falls, US
Data Scientistadt · Remote, US
Software Engineer, App Ads Bidding and OptimizationGoogle · Mountain View, US