Data Engineering Lead

dentsu•Chicago, IL

About The Position

The Data Engineering Lead is a player-coach role responsible for guiding engineering teams, shaping technical strategy, partnering with clients, and influencing how modern data platforms are built and operated. The role combines leadership with hands-on technical involvement to solve complex challenges and deliver measurable business outcomes across data engineering, cloud platforms, AI-enabled solutions, and customer data ecosystems. The role supports the development of Data Engineers through coaching, mentorship, career guidance, and growth opportunities. It oversees complex data engineering initiatives and partners with Solution Architects and stakeholders to deliver scalable, high-quality solutions. The Lead promotes engineering best practices and collaborates with client leaders, business stakeholders, architects, and delivery teams to turn business goals into engineering solutions. The role also evaluates and adopts emerging technologies, including AI-assisted engineering tools, cloud-native analytics platforms, warehouse-native AI solutions, and next-generation data architecture patterns.

Requirements

7+ years of experience in data engineering, including cloud-based data platforms, ELT/ETL processes, and production data pipelines.
Strong experience with platforms such as Snowflake, Databricks, Google BigQuery, Microsoft Fabric, Azure Synapse, or Amazon Redshift.
Advanced SQL and Python skills.
Experience with orchestration and transformation tools such as dbt, Apache Airflow, Azure Data Factory, Dagster, Prefect, or similar technologies.
Experience designing data models and customer-centric data solutions.
Understanding of data quality, lineage, observability, and governance best practices.
Experience with Git, CI/CD, and modern engineering workflows.
2+ years leading, mentoring, or managing data engineering teams.
Strong communication skills and the ability to collaborate with technical and non-technical audiences.

Nice To Haves

Experience with any combination of Snowflake, Databricks, dbt, Adobe Real-Time CDP, Salesforce Data Cloud, or related certifications.
Experience with Adobe Experience Platform (AEP), Real-Time CDP, Customer Journey Analytics, Salesforce Data Cloud, Braze, or other customer data platforms.
Experience with AWS, Google Cloud Platform, Microsoft Azure, or Infrastructure as Code technologies such as Terraform.
Experience with AI/ML technologies, warehouse-native AI capabilities, feature engineering, vector databases, or LLM-ready data architectures.
Experience with data privacy, governance, consent management, and secure data exchange practices.
Pre-sales, solution design, technical estimation, or consulting experience.

Responsibilities

Coach, mentor, and provide career guidance to Data Engineers, creating opportunities for team members to learn, contribute, and grow.
Oversee complex data engineering initiatives and partner with Solution Architects and stakeholders to deliver scalable, high-quality data solutions.
Guide technical decisions, manage delivery risks, and help ensure successful outcomes across multiple engagements.
Establish and promote engineering best practices, including CI/CD, testing, code reviews, observability, data quality, documentation, and governance.
Create repeatable engineering approaches that help teams deliver consistently and effectively.
Collaborate with client leaders, business stakeholders, architects, and delivery teams to translate business goals into actionable engineering solutions.
Communicate complex technical concepts to technical and non-technical audiences to support informed decisions and successful project outcomes.
Evaluate and adopt modern technologies, including AI-assisted engineering tools, cloud-native analytics platforms, warehouse-native AI solutions, and next-generation data architecture patterns.