Job Title: Data Engineer
You are a proactive and assertive data engineer who loves to anticipate and solve complex data challenges. You thrive in a fast-paced environment where you can achieve your goals with independence and creativity.
The Opportunity:
Join Conversion Logix as a Data Engineer, where you will play a pivotal role in building robust, goal-driven data pipelines and workflows that support our internal services and products. Your work will include designing, implementing, and maintaining scalable, fault-tolerant data pipelines, integrating third-party tools into a cohesive, scalable platform, and ensuring data governance and integrity. As a key member of our Data Intelligence Team, you will collaborate closely with data scientists, machine learning engineers, and cross-functional teams. We are committed to supporting your growth into becoming a leading expert in the data engineering field.
Who We Are:
Conversion Logix is a marketing and technology company dedicated to generating leads, appointments, and sales for multifamily housing, senior living, automotive dealerships, and local businesses. Our new Data Intelligence Team is focused on developing cutting-edge internal services and products that drive business growth and efficiency. We foster a collaborative environment where creativity and technical excellence are valued and encouraged.
What You'll Do:
- Data Services Delivery: Deliver high-quality data services promptly, ensuring data governance and integrity while meeting objectives and maintaining SLAs for data sharing across multiple products.
- Data Pipeline Development: Design, build, and maintain scalable, fault-tolerant data pipelines using orchestrators such as Flyte, Prefect, Airflow, or Mage on Kubernetes clusters.
- Data Modeling: Develop and optimize data models that support business reporting and analytics requirements.
- Architecture Development: Create effective architectures and produce key code components that contribute to the design, implementation, and maintenance of technical solutions.
- Third-Party Integration: Integrate a diverse network of third-party tools into a cohesive, scalable platform, optimizing code for enhanced scalability, performance, and readability.
- System Performance Improvement: Continuously improve system performance and reliability by diagnosing and resolving unexpected operational issues to prevent recurrence.
- Data Infrastructure Support: Support data infrastructure and the data team in designing, implementing, and deploying scalable, fault-tolerant pipelines that process large, diverse datasets into accessible data models in production.
- Kubernetes Management: Deploy, manage, and troubleshoot data workflows within Kubernetes environments.
- Collaboration: Collaborate with cross-functional teams to understand data flows and design, build, and test optimal solutions for engineering challenges.
- Agile/Scrum Participation: Operate within an Agile/Scrum framework, working closely with Product and Engineering teams to deliver value across multiple services and products.
- Platform Influence: Influence and shape the enterprise data platform and services roadmap, architecture, and design standards. Work with technology leaders and team members to adapt and enhance the architecture to meet evolving business needs.
- Compute Platforms: Utilize data compute platforms like Spark and preferably Ray to process and analyze large datasets efficiently within Kubernetes.
- Programming: Write efficient SQL queries and Python code for data manipulation and analysis.
- DevOps Integration: Apply DevOps practices to automate and enhance data engineering workflows, including deployment and monitoring within Kubernetes.
- Rigorous Testing: Ensure that your work undergoes rigorous testing through repeatable, automated methods to maintain data integrity and reliability.
- Continuous Improvement: Stay updated with the latest industry trends and technologies to continuously improve data engineering practices.
Must-Haves in a Candidate:
- Experience: 5+ years of hands-on experience in data engineering with a focus on data modeling and pipeline creation.
- Proficiency in SQL and Python: Strong coding skills for data manipulation and analysis.
- Data Pipeline Orchestrators: Experience with tools like Flyte, Prefect, Airflow, or Mage.
- Data Compute Platforms: Familiarity with Spark and preferably Ray.
- Cloud Experience: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud, particularly their Kubernetes services (e.g., EKS, AKS, GKE).
- Kubernetes Expertise: Proven experience working with Kubernetes for managing data workflows.
- Big Data Technologies: Experience with big data tools and frameworks.
- Infrastructure as Code: Knowledge of tools like Terraform or Ansible.
- Testing and Quality Assurance: Experience with automated testing methods to ensure code reliability and performance.
- Agile Methodologies: Familiarity with Agile/Scrum frameworks and collaborative development.
- Excellent Communication and Organization: Clear communication, good organizational skills, and sound decision-making abilities.
- Humility and Willingness to Learn: Openness to feedback and a strong desire for continuous learning and improvement.
Details:
- This is a full-time, salaried position. No agencies or part-timers.
- Remote position -candidates in Texas, Washington or Oregon preferred
