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Data Engineer

Job in Dearborn, Wayne County, Michigan, 48120, USA
Listing for: FastTek Global
Full Time position
Listed on 2026-06-02
Job specializations:
  • IT/Tech
    Data Engineer
Salary/Wage Range or Industry Benchmark: 110000 - 140000 USD Yearly USD 110000.00 140000.00 YEAR
Job Description & How to Apply Below
Position: 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 lake houses 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 Big Query. 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., Big Query, 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 Big Query ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a Big Query 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…
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