Data Engineer - Temporary Worker

Dentsu is a global marketing and advertising agency, and they are seeking a Data Engineer to build and operate modern data platforms for customer data warehouses. The role involves ingesting data from various sources, developing scalable data pipelines, and collaborating with cross-disciplinary teams to enhance customer engagement and insights.

Responsibilities:

• Contribute to the design of cloud data warehouse architecture and the conformed customer data model
• Build and maintain SQL transformations, data models, and ELT/data pipelines that ingest, rationalize, and enrich customer data
• Leverage Merkle's common frameworks, reusable components, and AI-assisted development tools to accelerate delivery
• Apply development standards, version control, and engineering best practices
• Engineer customer features and assemble unified customer profiles for downstream CDP, CRM, analytics, and data science consumption
• Perform development, unit and data-quality testing, and peer code review
• Automate and orchestrate data pipelines, and manage code from development to production through source control and CI/CD
• Use AI coding assistants and GenAI tooling responsibly to improve productivity, code quality, and feature development
• Deliver high-quality, well-tested code and design documentation with minimal supervision
• Be versatile and willing to take on new challenges across different projects and technologies
• Ship reliable, automated pipelines that meet data-quality and delivery expectations
• Mentor and provide technical guidance to other developers
• Maintain a high sense of urgency to deliver on time

Requirements:

• 2–4+ years building data pipelines and SQL transformations on a major RDBMS and/or cloud data warehouse (e.g., Snowflake, BigQuery, Databricks, Amazon Redshift, Azure Synapse)
• Strong SQL and database programming skills, including query performance tuning and optimization
• Hands-on experience with ELT/ETL and data transformation tooling (e.g., dbt, Talend, Informatica, Matillion, SSIS)
• Strong understanding of relational and dimensional data modeling, and of conformed / customer (Customer 360) data models
• Proficiency with source control (Git) and branching workflows, plus CI/CD for automated dev-to-prod deployment
• Understanding of secure data exchange and file management (sFTP, PGP encryption) and of data privacy and governance practices
• Experience with data pipeline orchestration and automation (e.g., Apache Airflow, Dagster, Prefect, dbt Cloud, Azure Data Factory)
• Familiarity with software engineering methodologies (Agile/Scrum), issue tracking (Jira), and the full software development lifecycle
• Understanding of core cloud and IT concepts — compute, storage, networking, and backups
• Proficiency in Python for data engineering and scripting; comfort with Bash and command-line workflows
• Solid communication skills, both verbal and written
• Experience using AI coding assistants (e.g., GitHub Copilot) and a working knowledge of GenAI/LLM concepts
• Experience with cloud platforms (AWS, GCP, Azure) and infrastructure-as-code (e.g., Terraform)
• Experience with Customer Data Platforms (CDPs), identity resolution, and database marketing solutions
• Experience with Business Intelligence tools: Tableau, Power BI, Looker, or MicroStrategy
• Experience with campaign and engagement tools: Adobe Campaign, Salesforce Marketing Cloud, Braze, Unica, or RedPoint
• Experience engineering features for machine learning and applying AI/ML to marketing use cases (propensity, segmentation, personalization)
• Familiarity with data observability, testing, and DataOps practices