Data Engineer - Journeyman at Modern Technology Solutions Inc

Dayton, Ohio, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Jan, 26

Salary

0.0

Posted On

25 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Data Warehousing, Data Modeling, Schema Design, Query Optimization, Google BigQuery, Apache Airflow, Apache Beam, PostgreSQL, Python, CI/CD, Git, Infrastructure-as-Code, Google Cloud Platform, Docker, Kubernetes, Machine Learning

Industry

Space Research and Technology

Description

Data Warehousing & Analytics (Core Requirement): Advanced proficiency in SQL with extensive experience in cloud data warehousing. Must have deep knowledge of data modeling, schema design, and query optimization. ○ GCP Preference: Significant, hands-on experience with Google BigQuery (partitioning, clustering, BQ scripting, cost optimization) is essential. Modern Data Pipeline Expertise (Must-Have Foundation): Demonstrable expertise in building, deploying, and scaling complex data pipelines using at least one of the following foundational technologies: ○ Orchestration: Deep expertise in Apache Airflow (designing, deploying, scaling, and managing complex DAGs). GCP Preference: Hands-on experience with Google Cloud Composer. ○ Processing: Proven ability to build and optimize robust, high-throughput batch and streaming data pipelines using Apache Beam. GCP Preference: Direct experience managing Beam pipelines at scale using Google Cloud Dataflow. ○ (Note: This role offers the flexibility to architect solutions and select the appropriate services to meet project requirements). Relational Database Expertise (Must-Have Foundation): Solid foundation and practical experience with RDBMS architecture, management, and optimization, specifically with PostgreSQL. ○ GCP Preference: Familiarity with managed database services, particularly Google Cloud SQL (for PostgreSQL or MySQL), is a significant advantage. Core Programming & Infrastructure: Fluency in Python (preferred) or Java for data pipeline development. Strong understanding of CI/CD, Git, and Infrastructure-as-Code (e.g., Terraform). Cloud Architecture & GCP (Highly Desirable): Broad experience in architecting, building, and managing solutions on a major cloud platform, with a strong preference for Google Cloud Platform (GCP) beyond just its data services. Containerization & Kubernetes (Highly Desirable): Understanding of container concepts (Docker) and practical experience with Kubernetes, particularly Google Kubernetes Engine (GKE). ML Engineering Exposure (Plus): Experience supporting machine learning workflows, including data preparation, feature engineering, and operationalizing data pipelines for ML models. ○ GCP Preference: Any exposure to Google Cloud Vertex AI (Pipelines, Feature Store, Training) is a major plus. Bachelor's or master's degree in computer science, Data Science, or a related field. 5-8 years of experience of hands-on experience in data engineering, demonstrating a clear progression in designing, building, and maintaining scalable data-intensive systems. Demonstrated ability to communicate complex data issues to both technical and non-technical stakeholders.

Responsibilities

The Data Engineer will be responsible for building, deploying, and scaling complex data pipelines and managing data warehousing solutions. They will also architect solutions and select appropriate services to meet project requirements.