Senior Data Engineer
About The Position
Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world. We are seeking an experienced Data Engineer to join our data team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data integration processes, and data infrastructure on AWS cloud. You will collaborate closely with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization. Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Requirements
- 8+ years experience as a Data Engineer working with AWS cloud services.
- Hands-on experience with AWS services such as S3, Glue, Lambda, Redshift, and related data platform tools.
- Experience building data pipelines using Databricks, Apache Spark, and SQL.
- Experience with Apache Airflow for workflow orchestration.
- Strong understanding of data modeling, data lake/lakehouse architectures, and ETL/ELT frameworks.
- Experience with CI/CD pipelines and version control systems (Git).
- Exposure to Generative AI or LLM-based applications.
- Experience supporting data pipelines for AI/ML workloads.
- Familiarity with vector databases, embeddings, and Retrieval-Augmented Generation (RAG) architectures.
- Experience working with LLM APIs or AI frameworks such as LangChain.
- Understanding of MLOps workflows and model deployment pipelines.
Responsibilities
- Design, develop, and deploy end-to-end data pipelines on AWS cloud infrastructure using services such as Amazon S3, AWS Glue, AWS Lambda, Amazon Redshift, etc.
- Implement data processing and transformation workflows using Databricks, Apache Spark, and SQL to support analytics, reporting, and AI-driven use cases.
- Build and maintain orchestration workflows using Apache Airflow to automate data pipeline execution, scheduling, and monitoring.
- Support data preparation and ingestion for AI/ML and Generative AI workloads, including handling structured and unstructured datasets.
- Enable data pipelines that support LLM-based applications, vector embeddings, and knowledge retrieval systems.
- Lead the migration of legacy data systems to modern cloud-based data architectures.
- Develop and maintain CI/CD pipelines for data workflows and platform automation.
- Collaborate with data scientists, ML engineers, and AI teams to ensure data availability for model training, inference, and GenAI applications.
- Optimize data pipelines for performance, reliability, scalability, and cost-effectiveness using AWS best practices.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
11-50 employees
