(AME4631) Data Analyst / CDP Developer

Delta Computer Consulting•Torrance, CA

17h•Onsite

About The Position

The Data Analyst / CDP Developer analyzes, designs, develops, integrates, and enhances customer data platform (CDP) solutions; improves data quality; builds scalable data solutions; and addresses end-to-end data challenges. The role works with customer, event, and large-scale datasets, designing and optimizing SQL queries and transformations across Presto, Hive, and NoSQL systems, and developing Python solutions for data processing, automation, workflow integration, and APIs. It also designs cloud data solutions, primarily on AWS, and CDP data models, schemas, identity-resolution logic, customer profiles, workflows, and integrations with CRM and downstream systems. The position collaborates with engineering, analytics, marketing, CRM teams, and subject matter experts to interpret requirements and validate technical solutions. It documents data processes, business rules, transformation logic, integrations, system requirements, and workflows. The role applies data-quality frameworks, monitoring, alerts, pipeline observability, CI/CD, Git-based version control, and privacy and compliance requirements such as GDPR, CCPA, and PII handling. The position also supports production data environments by diagnosing and resolving pipeline failures, ingestion issues, integration defects, and workflow errors. It performs root-cause analysis using logs, metadata, lineage, and monitoring tools; implements corrective and preventive changes; and documents issues and solutions. The position is located in Torrance, California.

Requirements

Bachelor's degree in Data Science, Computer Science, Information Systems, Engineering, or a related field.
At least 5 years of combined experience across data analysis, data engineering, data development, or data operations.
Advanced SQL and query-optimization skills, including Presto, Hive, and NoSQL.
Experience designing complex SQL queries and transformations for large-scale datasets, including complex joins, window functions, CTEs, and performance optimization.
Experience with Customer Data Platforms (CDPs) and CRM systems, including designing integrations and configuring solutions to unify and activate customer data.
Proficiency in Python development for data processing, automation, workflows, and API integrations.
Experience working with customer data, event data, and large-scale datasets.
Experience designing and implementing data solutions in cloud environments such as AWS, GCP, or Azure, including S3, IAM, and cloud data storage concepts.
Ability to perform exploratory data analysis, validate data quality and transformation logic, identify technical data issues, and communicate findings to stakeholders.
Understanding of data modeling, schema design, identity resolution, and profile unification.
Ability to document data processes, transformations, system requirements, integration logic, and technical designs in Confluence or similar platforms.
Strong communication skills and ability to work with cross-functional teams.
Experience with data-quality frameworks, monitoring, alerting, and pipeline observability.
Experience with CI/CD, Git, and version-control best practices.
Understanding of GDPR, CCPA, and PII-handling requirements.
Experience analyzing and resolving complex technical issues in production data platforms, pipelines, and integrations.
Strong technical problem-solving skills and ability to deliver end-to-end data solutions.
Excellent attention to detail, particularly with customer-level and production data.
Proactive approach to improving data quality, automation, and system reliability.
Ability to collaborate with subject matter experts to understand domain context, analyze and validate requirements, and align technical solutions with those requirements.

Nice To Haves

Hands-on experience with Treasure Data CDP or similar CDPs such as Amperity, mParticle, or Tealium.
Experience designing, configuring, and maintaining workflow orchestration using Digdag Workflows or similar tools.

Responsibilities

Analyze customer data, event data, and large-scale datasets to define requirements, validate data quality and transformations, identify patterns and anomalies, and support data-solution design.
Design, write, optimize, and maintain SQL queries and transformations across Presto, Hive, and NoSQL systems.
Develop and modify Python scripts and solutions for data processing, automation, workflow integration, and API integrations.
Design and implement data solutions in cloud platforms, primarily AWS, applying cloud architecture, storage, security, and integration principles.
Perform exploratory data analysis and translate stakeholder requirements into data and system requirements.
Document data processes, business rules, transformation logic, requirements, integrations, and workflow designs in Confluence or similar platforms.
Collaborate with engineering, analytics, marketing, CRM teams, and subject matter experts to define requirements and validate solution accuracy.
Design, configure, and enhance CDP workflow orchestration using tools such as Digdag and TD Workflows.
Design, enhance, and monitor data-quality frameworks, including validation logic, automated checks, alerts, and pipeline observability.
Apply CI/CD and Git-based version-control practices to SQL, Python scripts, workflow configurations, and technical documentation.
Incorporate privacy and compliance requirements, including GDPR, CCPA, and PII handling, into data processing and solution design.
Design end-to-end data solutions spanning ingestion, integration, transformation, analysis, and activation.
Design and refine data models, schemas, identity-resolution logic, and customer profile structures.
Define technical specifications and integration logic for data flows between CDP, CRM, cloud, API, and downstream systems.
Evaluate approaches to ingestion, transformation, integration, and workflow design based on scalability, performance, data quality, and implementation requirements.
Assess technical feasibility, dependencies, data constraints, and implementation considerations for new CDP and data-platform requirements.
Improve automation, data quality, workflow architecture, reliability, and system performance.
Communicate insights and technical concepts to technical and non-technical audiences.
Partner with subject matter experts to understand business context, refine requirements, evaluate approaches, and validate technical accuracy.
Analyze, diagnose, and resolve data pipeline failures, ingestion issues, integration defects, and workflow errors.
Perform root-cause analysis using logs, pipeline metadata, data lineage, and system monitoring tools.
Support production environments by analyzing pipeline behavior, validating transformations and outputs, and identifying changes to improve data accuracy, availability, and reliability.
Implement code, configuration, workflow, and data-processing changes to resolve issues and prevent recurrence.
Document issues, root causes, solutions, and preventive design changes.