The Role:
Reporting to the Data \& Analytics Director, this position is for a Data Engineer who is passionate about building robust, scalable data solutions and light weight AI applications. While our ecosystem is built on the Google Cloud Platform (GCP), we value strong engineering fundamentals and welcome candidates with experience in similar technologies from other cloud environments (like AWS or Azure).
You will work closely with data analysts, media teams and business stakeholders to build the foundational technology that drives business growth and operational efficiency. A key focus of this role will be developing and deploying light weight internal tools on Google Cloud Run that are powered by Generative AI. This role offers the opportunity to directly collaborate with clients and vendors, implementing data \& analytics strategies for OMG’s clients. You will contribute to our mission of empowering our agencies with advanced data solutions.
Key Responsibilities:
Data Pipeline Architecture \& Development:
Design, build, and maintain resilient and scalable ETL/ELT pipelines on GCP to process data and load it into BigQuery.
Workflow Automation \& Solution Design:
Proactively identify opportunities to automate day-to-day workflows and repetitive tasks across the business. Design and implement automated solutions that reduce manual effort, increase efficiency, and allow teams to focus on higher-value activities.
Develop Lightweight AI-Powered Tools:
Build simple, internal-use web tools using Python frameworks (e.g., Streamlit, Flask). The role involves writing scripts and developing lightweight applications that integrate Generative AI models (e.g., Google's Gemini via Vertex AI) to support tasks like natural language querying, report summarization, and basic insight generation.
Application Deployment:
You will be responsible for containerizing these AI-powered applications with Docker and deploying them on Google Cloud Run, our primary service for hosting container-based applications and APIs.
Data Governance \& Quality:
Implement and automate data quality checks to ensure the accuracy and consistency of data within our BigQuery data warehouse
Technical Strategy \& Innovation:
Lead the exploration and implementation of Generative AI use cases within our data platform. You will evaluate new models and services to build innovative solutions that create tangible business value.
Expected Qualifications:
3+ years of experience in a data engineering or a similar software engineering role.
Strong programming skills in Python, with experience using data-related libraries (e.g., Pandas, Polars).
Expert-level SQL skills.
Proven experience with at least one major cloud platform (GCP, AWS, or Azure), with a willingness to specialize in the GCP ecosystem.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field is a plus.
Technical Proficiency \& Our Stack:
While direct experience with our specific tools is a plus, we value transferable skills and a strong foundation in equivalent technologies.
Data Warehousing:
Google BigQuery (Equivalent experience: Snowflake, Amazon Redshift, Azure Synapse)
Data Processing \& Orchestration:
Cloud Composer (Airflow), Cloud Dataflow (Spark), and Cloud Functions (Equivalent experience: AWS Lambda, Azure Functions)
Application \& API Deployment:
Google Cloud Run, using Docker for containerization. (Equivalent experience: Kubernetes, AWS Fargate, Azure Container Apps)
Generative AI:
Experience or strong interest in integrating large language models (LLMs) via APIs (e.g., Google Vertex AI, OpenAI).
Web Application Development:
Experience in building lightweight data applications or internal tools with Python frameworks (e.g., Streamlit, Flask).
Domain Knowledge: Familiarity with digital marketing tools and ad platforms (e.g., Google Ads, Meta Ads, Google Analytics) is a plus.
Who You Are:
Analytical Mindset: You have strong analytical and problem-solving abilities to tackle complex data challenges.
Excellent Communicator: You can effectively partner with both technical and non-technical stakeholders to translate business needs into technical solutions.
Strong Sense of Project Ownership: You can take technical projects from conception to completion with autonomy and accountability.