Data Engineer

Job Highlights

1. Build Autonomous Pipelines: Go beyond manual ETL by integrating AI-driven anomaly detection and automated schema evolution into our core pipelines.
2. AI-Enhanced Development: Utilize AI-assisted coding and automated documentation tools to accelerate the development of complex data architectures.
3. Modern Data Stack: Get hands-on experience with production-grade tools in GCP (BigQuery, Cloud Composer) or AWS (Redshift, Glue).
4. High-Impact Engineering: Work on scaling challenges where you optimize for performance and cost in a real-world, high-volume data environment.
5. Mentorship-Driven Growth: You’ll work alongside Senior Architects who prioritize your technical growth and system design skills.

Job Description

The Junior Data Engineer will assist in designing, building, and optimizing our core data architecture. You will contribute to developing scalable data pipelines that transform raw data into high-quality, actionable insights. In this role, you will focus on building the data foundation for AI initiatives, helping to manage the infrastructure required for Generative AI, Large Language Models (LLMs), and advanced analytics.

Responsibilities

Data Architecture & Pipeline Engineering

• Pipeline Development: Support the design and maintenance of scalable ETL/ELT pipelines for structured and unstructured datasets.

• AI Data Readiness: Assist in building data ingestion flows for Vector Databases and supporting RAG (Retrieval-Augmented Generation) architectures.

• Data Modeling: Contribute to the creation of robust data models and Feature Stores that serve both traditional analytics and machine learning workloads.

Data Quality & Operations

• Validation & Cleaning: Implement automated scripts to ensure high data integrity, reliability, and performance across the platform.

• Cloud Optimization: Assist in monitoring cloud resource usage (GCP/AWS) to ensure cost-efficiency and low-latency data access.

• Engineering Collaboration: Work closely with senior engineers to document data lineage and ensure the architecture is built for long-term scalability.

Jobs Qualifications:

Experience: Entry-level to 2 years of experience in data engineering, backend development, or a related technical internship.

1. Portfolio: Demonstration of coding ability through a Github repository or a portfolio of data projects (e.g., building a personal API, a data scraper, or a small-scale ETL project).

2. Education: Bachelor’s degree in Computer Engineering, Computer Science, Statistics, or a related technical field.

3. Technical Skills:

Python: Solid foundation in writing clean, modular Python code.

SQL: Proficiency in writing and optimizing complex queries for data analysis.

Cloud Knowledge: Familiarity with at least one major cloud provider (GCP or AWS) and basic understanding of services like BigQuery, Redshift, or S3.

1. AI Awareness: Interest in how data engineering supports AI; basic knowledge of unstructured data or vector search is a plus.

What We Offer

• Hands-on Multi-Cloud Experience: Get direct exposure to large-scale data environments on GCP/AWS and modern orchestration tools.

• Innovative Tech Stack: Work with cutting-edge tools at the intersection of Big Data and AI, including Vector DBs and automated data quality frameworks.

• Growth & Mentorship: A supportive environment where you will learn from senior engineers and have a clear path for professional development.

• Impactful Work: See your data pipelines directly power real-time marketing decisions and AI-driven products.