Lead Data Engineer

Role: Lead Data Engineer with Databricks & AWS or if you find any Architects do not hold them please share it

Location: St. Louis, MO

Work type: 3 days onsite per week

Skill Requirements

• Primary focus:
  • 60% Databricks
  • 40% ETL experience (any ETL tool/platform)
• AWS is preferred but not mandatory.
• Project involves migration from Teradata to Databricks .

JD:

Key Responsibilities:
1. Design and architect scalable, secure, and high-performance AWS and Databricks solutions, leveraging AWS Networking, Kinesis, Elastic Cache, Redshift, EKS, Cognito, and PaaS services
2. Develop and maintain robust ETL/ELT pipelines using PySpark and Python within Databricks, including notebooks, jobs, Delta Lake tables, and Unity Catalog for governance
3. Implement medallion architecture (bronze, silver, gold layers) and optimize Spark jobs for performance, cost, scalability, and reliability, addressing partitioning, skew, caching, and adaptive query execution
4. Provision and manage cloud infrastructure using Terraform for Databricks workspaces, clusters, jobs, storage (ADLS/S3), networking, IAM roles and permissions, and related resources on Azure and AWS
5. Write efficient SQL queries for data transformation, validation, and analytics within Databricks, ensuring data quality, lineage, security, and compliance
6. Implement and maintain CI/CD pipelines using Jenkins and GitHub Actions for automated testing and deployment of notebooks, jobs, Delta Live Tables, and Terraform configurations
7. Integrate data from diverse sources (databases, APIs, streaming, files) into cloud storage and processing layers
8. Optimize cloud costs through auto-scaling clusters, spot instances, job scheduling, and efficient resource usage
9. Monitor pipeline performance, troubleshoot failures, and implement alerting and observability using Databricks tools, cloud monitoring services, or third-party solutions

Required Skills:
1. Advanced proficiency in AWS Networking and VPC design
2. Expertise in AWS Kinesis for real-time data streaming and analytics
3. Deep experience with AWS Elastic Cache for distributed caching
4. Extensive experience with AWS Redshift for data warehousing
5. Strong knowledge of AWS PaaS services including Lambda, S3, and Glue
6. Hands-on experience with AWS EKS for container orchestration
7. Proficiency in AWS Cognito for identity and access management
8. Expertise in Databricks, including Delta Lake, Unity Catalog, and Delta Live Tables
9. Strong Python and PySpark skills for distributed data processing
10. Advanced SQL skills for complex querying and optimization
11. Proficiency in Terraform for infrastructure automation and management
12. Experience with CI/CD tools such as Jenkins and GitHub Actions

Preferred Skills:
1. Knowledge of Azure Data Lake, Data Factory, Synapse, and Key Vault
2. Understanding of big data modeling (star/snowflake, dimensional) and lakehouse architecture
3. Experience with performance tuning in Spark and Databricks environments
4. Familiarity with data governance, lineage, and compliance in cloud environments

Desired Qualifications:
1. Bachelor's degree in Computer Science, Information Technology, or a closely related discipline
2. AWS Certified Solutions Architect Professional
3. Databricks Certified Data Engineer Professional or Apache Spark certification

Note: Momento USA is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.