Data Engineer #1054989

Dearborn, Michigan Data Engineer #1054989 Job Description Employees in this job function are responsible for designing, building, and maintaining data solutions including data infrastructure, pipelines, and other systems for collecting, storing, processing and analyzing large volumes of data efficiently and accurately. Key Responsibilities Collaborate with business and technology stakeholders to understand current and future data requirements. Design, build and maintain reliable, efficient and scalable data infrastructure for data collection, storage, transformation, and analysis. Plan, design, build and maintain scalable data solutions including data pipelines, data models, and applications for efficient and reliable data workflow. Design, implement and maintain existing and future data platforms like data warehouses, data lakes, and data lakehouses for structured and unstructured data. Design and develop analytical tools, algorithms, and programs to support data engineering activities, such as writing scripts and automating tasks. Ensure optimum performance and identify improvement opportunities. Skills Required GCP – Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions. For example, designing and implementing a cloud‑native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub. Big Data – Experience working with large‑scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery. For example, building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics. Data Warehousing – Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift). For example, modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behavior. Artificial Intelligence & Expert Systems – Experience developing or integrating AI/ML models and rule‑based expert systems. For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions. API – Experience designing, building, and consuming RESTful or gRPC APIs. For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices. Skills Preferred Google Cloud Platform – Familiarity with advanced GCP services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse. Experience Required Senior Engineer – 10+ years of data engineering work experience. Experience Preferred As a Senior Data Engineer, you will architect and scale end‑to‑end data pipelines on GCP, transforming complex telemetry and enterprise data into high‑quality, analytics‑ready assets using Medallion architectures. You will lead the implementation of robust CI/CD workflows, rigorous data governance, and security controls while mentoring junior talent and driving engineering best practices. By collaborating with cross‑functional stakeholders and optimizing cloud performance, you will ensure the data platform remains secure, cost‑effective, and highly available to power critical business insights. Operational Excellence: Using Terraform, Git, and Airflow to ensure reproducible, secure, and cost‑optimized cloud infrastructure. Governance & Quality: Prioritizing data lineage, PII protection, and observability to maintain high trust in data assets. Collaboration: Acting as a bridge between technical teams (Data Science, Security) and business stakeholders to deliver self‑service analytics. Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval‑Augmented Generation (RAG) systems. Proven experience in building and deploying RAG systems, including the use of vector databases. Proficiency in Python programming. Solid experience with SQL for data manipulation and querying. Hands‑on experience with Google Cloud Platform services relevant to AI/ML. Basic understanding and practical experience with machine learning model fine‑tuning. Familiarity with data engineering concepts and practices. Expertise in prompt engineering techniques for interacting with LLMs. Experience with the OpenAI SDK. Experience developing robust APIs, preferably with FastAPI. Proficiency with version control systems (e.g., Git). Experience with containerization technologies (e.g., Docker). Education Required Bachelor's Degree Education Preferred Certification Program Benefits Medical and Dental (FastTek pays majority of the medical program) Vision Personal Time Off (PTO) Program Long Term Disability (100% paid) Life Insurance (100% paid) 401(k) with immediate vesting and 3% (of salary) dollar‑for‑dollar match #J-18808-Ljbffr FastTek Global