Junior Data Engineer (GCP Cloud Migrations)

About Mod Op

Mod Op is a full-service advertising agency able to offer clients a full suite of solutions. Mod Op can offer you access to low-cost, high-quality health care options and a team of enthusiastic, collaborative and motivated coworkers who see career development and personal development as intertwined.

At Mod Op, we’re more than just an agency—we’re a team of forward-thinking professionals who are passionate about driving client success. We believe in fostering meaningful relationships, collaborating across disciplines, and delivering impactful solutions that help businesses grow. If you are a strategic thinker with a passion for building lasting client partnerships, we’d love to hear from you. Join us and be part of a company that values innovation, creativity, and excellence in everything we do.

About the role

As a Junior Data Engineer with 1–2 years of experience, you will support the development of scalable data systems and contribute to AI and machine learning initiatives and play a crucial role in modernizing data infrastructure for our largest enterprise clients, including major initiatives in the telecommunications sector. This role is highly focused on core data engineering, cloud migration strategies, and building scalable, reliable data pipelines. You will work directly on migrating complex, legacy data systems into modern Google Cloud Platform (GCP) environments. If you love solving puzzles, optimizing SQL queries, and want hands-on experience with enterprise-scale data architecture, this is the perfect place to kickstart your career.

The position operates under a hybrid work model, requiring in-office presence at the Dallas, Texas location two days per week, with the remaining days worked remotely. 

What you'll do

Data Pipeline Development: 
Build, monitor, and maintain robust ETL/ELT pipelines using GCP services like Dataflow and Cloud Composer (Apache Airflow). 

Data Integration: 
Work with structured and unstructured data sources, including CRM systems, marketing platforms, APIs, and internal business systems, to support analytics and AI-driven applications. 

Database Management: 
Develop and optimize queries for SQL and NoSQL databases such as Teradata, BigQuery, and cloud data warehouses. 

Machine Learning Implementation: 
Support the development and deployment of machine learning models using Python and data science libraries (Pandas, NumPy) and cloud AI services such as GCP Vertex AI, AWS SageMaker, or Azure ML. 

AI-Enabled Data Workflows: 
Assist in building data pipelines that support predictive analytics, automation, and AI-driven insights. 

Data Visualization: 
Build and maintain dashboards using Google Looker and Tableau to help business and marketing teams understand and act on data insights. 

Collaboration: 
Work closely with data engineers, analysts, data scientists, and marketing teams to understand business requirements and support the development of data and AI solutions. 

Required Qualifications

Experience: 1–2 years of hands-on experience in a data-focused role (Data Analyst, Junior Data Engineer) or a strong portfolio of cloud-based data projects. 

The Core Stack: Strong proficiency in SQL (you must know your way around window functions, joins, and query optimization) and Python for data manipulation. 

Cloud Exposure: Foundational, hands-on familiarity with Google Cloud Platform (GCP)—specifically BigQuery or storage buckets.

Data Awareness: A clear understanding of data warehousing concepts (schemas, star/snowflake methodologies, ETL vs. ELT).

Problem-Solving Mindset: Ability to trace data errors, debug pipeline failures, and a desire to understand how data moves across an enterprise ecosystem.

Data Visualization: 
Experience building dashboards and reports using Google Looker or Tableau. 

Machine Learning Knowledge: 
Basic understanding of machine learning workflows, model training, evaluation, and deployment in cloud environments. 

Preferred Qualifications 

Experience with data warehousing platforms such as Snowflake or Redshift. 
Exposure to Apache Spark, Airflow, or other orchestration tools. 
Familiarity with MLOps practices and cloud-based ML platforms. 
Understanding of data governance, security, and compliance practices. 
Active certifications, such as the Google Cloud Digital Leader or Associate Cloud Engineer.

Mod Op, LLC provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.