Senior Ops Data Engineer
Indexed description
Our unique data and solutions empower over 4,300 customers globally, including industry giants like Google, eBay, and Adidas, to make game-changing decisions that drive their digital strategies.
In 2021, we went public on the New York Stock Exchange, and we continue to reach new heights! Come work alongside Similarwebbers across the globe who are bright, curious, practical, and good people.
Role Summary
We are looking for a Data Engineer to join our team and play a key role in designing, building, and maintaining scalable, cloud-based data pipelines. You will work with AWS services , Airflow and databricks to integrate, process, and analyze large datasets, ensuring data reliability and efficiency.
Your work will directly impact business intelligence, analytics, and data-driven decision-making across the company.
What You’ll Do
- ETL & Data Processing: Develop and maintain ETL processes, integrating data from various sources (APIs, databases, external platforms) using Python, SQL, and cloud technologies.
- Utilize LangGraph and other frameworks to create state of the art AI agents that integrate various tools and data sources.
- Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
- Data Modeling: Design and maintain logical and physical data models to support business needs.
- Optimization & Scalability: Improve process efficiency and optimize runtime performance to handle large-scale data workloads.
- Collaboration: Work closely with BI analysts and business stakeholders to define data requirements and functional specifications.
- Monitoring & Troubleshooting: Ensure data integrity and reliability by proactively monitoring pipelines and resolving issues.
- Education & Experience:
- BSc in Computer Science, Engineering, or equivalent practical experience.
- 5+ years of experience in data engineering or related roles.
- Technical Expertise:
- Proficiency in Python for data engineering and automation.
- Experience with Big Data technologies such as Spark, Databricks, DBT, and Airflow.
- Hands-on experience with AWS services (S3, Redshift, Glue, Managed Airflow, Lambda)
- Knowledge of Docker, Terraform, Kubernetes, and infrastructure automation.
- Strong understanding of data warehouse (DWH) methodologies and best practices.
- Soft Skills:
- Strong problem-solving abilities and a proactive approach to learning new technologies.
- Excellent communication and collaboration skills, with the ability to work independently and in a team.
- Experience with LangGraph, LangChain, or similar frameworks for building AI agents.
- Understanding of LLMs, prompt engineering, and AI agent architectures.
- Familiarity with K8s for infrastructure as code.
- Experience with JavaScript, React, and Node.js.
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