Data Engineer

• *SCRY AI** is an innovative AI\-driven technology company focused on building intelligent, scalable, and high\-performance solutions. We work with modern technologies across Software Engineering, AI/ML, and Data to solve real\-world business problems and deliver impactful digital products. **Role Overview**

We are looking for a skilled **Data Engineer with 2\-3 years of experience** in building scalable data pipelines, backend services, and data processing systems. The ideal candidate should have strong hands\-on experience in **Python, SQL, ETL/ELT, API development, and database technologies**, with exposure to AI/ML applications, Agentic AI, and cloud or on\-premises deployments. **Key Responsibilities*** Design, develop, and maintain scalable **ETL/ELT pipelines** for processing structured and unstructured data.

• Build data ingestion and transformation workflows using **Python, SQL, and relevant data engineering frameworks**.
• Extract, clean, validate, normalize, and transform data from APIs, databases, files, and other sources.
• Design and optimize database schemas, data models, SQL queries, joins, indexes, and aggregations.
• Work with relational databases such as **PostgreSQL/MySQL** and NoSQL databases where required.
• Develop batch and incremental data processing pipelines with appropriate error handling and recovery mechanisms.
• Implement data quality checks, validation rules, deduplication, and reconciliation to ensure data accuracy and consistency.
• Integrate data pipelines and AI applications with **SQL/NoSQL databases, APIs, enterprise systems, and customer\-specific data sources**.
• Develop **Agentic AI and workflow automation** solutions using frameworks such as LangChain/LlamaIndex, including tool calling and external system integrations.
• Build and optimize backend services and APIs to support data processing, AI/ML workflows, and enterprise integrations.
• Work directly with customers and stakeholders to understand requirements, troubleshoot issues, demonstrate solutions, and translate business needs into technical implementations.
• Take AI/ML solutions from **POC to production**, focusing on scalability, reliability, latency, monitoring, and maintainability.
• Deploy and troubleshoot data and AI applications using **Docker across cloud, on\-premises, and customer environments**.
• Implement logging, monitoring, alerting, and performance optimization for data pipelines and production applications.
• Follow engineering best practices using **Git, CI/CD, automated testing, and documentation**, collaborating with Data Scientists, ML Engineers, Backend Engineers, and business teams.

• *Key Qualifications*** **2\-3 years of experience** in Data Engineering, Backend Engineering, ETL development, or a related role.
• Strong proficiency in **Python and SQL**, including complex queries, joins, CTEs, window functions, and query optimization.
• Hands\-on experience building **ETL/ELT pipelines**, data ingestion workflows, and transformation processes.
• Experience with relational databases, preferably **PostgreSQL/MySQL**, and database design principles.
• Understanding of data warehousing, dimensional modeling, indexing, partitioning, and incremental data processing.
• Experience integrating REST APIs, databases, CSV/Excel files, JSON, and other data sources.
• Strong experience with **FastAPI/Flask, microservices, Docker, and backend architecture**; Kubernetes is a plus.
• Experience with **AWS/Azure/GCP and cloud or on\-premises deployments**.
• Knowledge of **CI/CD, automated testing, Git, logging, monitoring, production debugging, and AI/ML inference optimization**.
• Exposure to **LLMs, Agentic AI, tool calling, workflow automation, and frameworks such as LangChain/LlamaIndex**.
• Strong **problem\-solving, communication, customer\-facing, and collaboration skills**, with the ability to work independently in fast\-paced environments.

• *Good to Have*** Experience with **Apache Spark/PySpark, Kafka, or similar distributed data processing and messaging technologies**.
• Exposure to cloud data platforms and warehouses such as **BigQuery, Redshift, or Azure Data Factory**.
• Experience preparing datasets for **AI/ML, GenAI, RAG applications, vector databases, or analytics pipelines**