The Role
Build and maintain scalable ETL/ELT pipelines, data ingestion workflows, backend services, APIs, and database systems. Clean, transform, validate, and model structured and unstructured data while implementing quality controls, monitoring, and production reliability. Develop Agentic AI and workflow automation integrations, deploy applications across cloud and on-premises environments, and collaborate with customers and cross-functional teams to deliver AI/ML solutions from proof of concept to production.
Summary Generated by Built In
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
Skills Required
- 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 or MySQL, and database design principles
- Understanding of data warehousing, dimensional modeling, indexing, partitioning, and incremental data processing
- Experience integrating REST APIs, databases, CSV or Excel files, JSON, and other data sources
- Strong experience with FastAPI or Flask, microservices, Docker, and backend architecture
- Experience with AWS, Azure, or 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 LangChain or LlamaIndex
- Strong problem-solving, communication, customer-facing, and collaboration skills
- Experience with Apache Spark or PySpark, Kafka, or similar distributed data processing and messaging technologies
- Exposure to BigQuery, Redshift, or Azure Data Factory
- Experience preparing datasets for AI/ML, GenAI, RAG applications, vector databases, or analytics pipelines
- Kubernetes experience
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The Company
What We Do
Scry AI is a research-led enterprise AI company that develops intelligent platforms for businesses in banking, financial services, insurance, logistics, and industrial sectors. Its suite includes Auriga for conversational AI, Collatio for document intelligence, and Concentio for cognitive IoT and operational intelligence. The platforms process fragmented data, automate document and workflow operations, support compliance, and generate actionable insights to improve enterprise efficiency.
