Senior AI Data Engineer
Acumenz Consulting · United States
- On site
- Contract
- United States
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About the role
Role: Senior AI Data Engineer
Location: Remote
Duration: 6 Months
Experience: 8-15 Years
We are seeking a Senior Data Engineer to design, develop, and optimize scalable data pipelines and analytics solutions on secure cloud platforms.
Key Responsibilities:
- Design and deploy scalable, fault-tolerant data pipelines and ETL/ELT workflows.
- Work with SQL, Python, PySpark, Spark, Kafka, Snowflake, Databricks, dbt, and Airflow.
- Develop data ingestion, transformation, warehousing, and analytics solutions.
- Apply modern data architectures including Data Vault, Star Schema, 3NF, and Medallion Architecture.
- Build and optimize cloud-native data solutions using AWS.
- Implement CI/CD, data governance, testing, monitoring, and performance optimization.
- Collaborate with business and technical stakeholders to gather requirements and deliver solution architectures.
- Support GenAI/AI-first development, including agents, embeddings, context retrieval, and AI workflows.
- Integrate enterprise platforms such as Microsoft Graph, Salesforce, ServiceNow, Jira, and SharePoint.
- Develop secure applications using Docker, Kubernetes, and CI/CD pipelines.
- Lead technical discussions and mentor/support data engineering teams.
Required Skills:
- 5–8 years of relevant Data Engineering experience.
- Expert in SQL, Python, PySpark, and data pipelines.
- Strong experience with AWS services such as EMR, Glue, Athena, Lambda, EC2, DynamoDB, IAM, CloudWatch, and CloudFormation.
- Experience with Spark, Kafka, Snowflake/Databricks, dbt, Airflow, and data streaming.
- Knowledge of SQL/NoSQL databases, ETL, BI, Linux, networking, and CI/CD.
- Experience with Agile/Jira and remote teams.
- Strong communication, leadership, and stakeholder management skills.
- AWS certification is a plus.