Manager of Data Engineering

TAG - The Aspen Group•Chicago, IL

36d•$160,000 - $190,000•Onsite

About The Position

As the Manager of Data Engineering at TAG you will be responsible for overseeing a team of data engineers and driving the development and maintenance of our data infrastructure. You will play a critical role in ensuring data quality, transformation, and orchestration while working closely with cross-functional teams to support data-driven decision-making. This position offers a unique chance to lead a team and contribute to the organization's data-driven success. While you will be in a leadership role you are expected to have hands on keyboard and also contributing to building data pipelines and support Capitalized and Operational Driven Projects.

Requirements

Bachelor's degree in a relevant field (e.g., Data Science, Computer Science, Business Analytics); master’s degree preferred.
Proven experience in data engineering, with at least 2 years in a leadership or managerial role.
Minimum of 5 years’ experience in a Data Engineering Role – with a preference of 7 years for highly qualified candidates.
Expertise in DBT for data transformation, including cataloging, lineage, freshness, and data quality checks.
Strong understanding of data governance principles and best practices.
Proficiency in Airflow and DAG development.
Experience working with Google Cloud Platform (GCP) technologies.
Excellent coding skills in languages such as Python and SQL.
Strong leadership, communication, and problem-solving skills.
Knowledge of Agentic AI, RAG and Vibe Coding Technologies and the implementation of those into the data practice

Nice To Haves

Preferably with Professional Data Engineer Certification

Responsibilities

Leadership, Mentorship and Day to Day Management of Data Engineering Pods and Resources
Development of DBT Pipelines for data transformation, including cataloging, lineage, freshness, and data quality checks. Implement best practices to optimize data transformation processes.
Code Reviews: Conduct thorough code reviews to ensure the quality, efficiency, and maintainability of data engineering code. Provide constructive feedback and mentor team members to improve their skills.
Governance and Best Practices: Hold the data engineering team accountable for adhering to code quality, dbt best practices, and data governance standards. Establish and enforce coding standards and guidelines.
Infrastructure Management: Collaborate with the Data Platform Team to define and maintain the infrastructure for data engineering code. Ensure scalability, reliability, and performance of data pipelines.
Airflow and DAG Quality: Implement and maintain Airflow workflows and DAGs (Directed Acyclic Graphs) to orchestrate data pipelines effectively. Ensure the quality and reliability of workflow scheduling and execution.
Google Cloud Technologies: Leverage Google Cloud Platform (GCP) technologies such as Pub/Sub, DataFlow, Cloud Functions, BigQuery, and Cloud Storage to design and optimize data solutions.
Team Leadership: Lead and mentor a team of data engineers, fostering a culture of collaboration, continuous learning, and innovation. Provide guidance on technical and career development.
Data Collaboration: Collaborate closely with data scientists, analysts, and other stakeholders to understand their data requirements and provide data engineering support.
Agentic AI and RAG Integrations: Stay updated with the latest advancements in Agentic AI and RAG Technologies. Evaluate and integrate as and when appropriate into data engineering processes to enhance data retrieval, generation and overall efficiency
Lead the integration of Vibe coding practices into data engineering processes. Define and enforce guardrails to ensure ethical and responsible usage of Vibe Coding, collaborate with cross functional teams to establish best practices and help mentor the team the important of better prompting, style guides and interfacing with tooling choices to get more accurate, smarter and better results