Data Engineer

InnoveusIT; St. Louis (MO); On-site; USD 140,000 - 190,000; Full time

Job summary

InnoveusIT in the United States (Missouri) is seeking an experienced Senior Cloud Data Engineer to design and implement scalable AWS and Databricks solutions that ensure security, reliability, and high performance.

You will lead ETL/ELT development with PySpark, manage medallion architecture, and optimize Spark workloads while governing data with Unity Catalog and Delta Lake. This role also covers Terraform-based infra, CI/CD with Jenkins and GitHub Actions, and cost-efficient cloud management.

Qualifications

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

Responsibilities

• Design and architect scalable, secure, and high-performance AWS and Databricks solutions.
• Develop and maintain robust ETL/ELT pipelines using PySpark and Python within Databricks.
• Implement medallion architecture (bronze, silver, gold) and optimize Spark jobs for performance and cost.
• Provision and manage cloud infrastructure using Terraform for Databricks workspaces and AWS resources.
• Write efficient SQL queries for data transformation and analytics within Databricks.
• Implement and maintain CI/CD pipelines using Jenkins and GitHub Actions.
• Integrate data from diverse sources into cloud storage and processing layers.
• Optimize cloud costs through auto-scaling clusters and efficient resource usage.
• Monitor pipeline performance and implement observability and alerting.

Skills

AWS Networking

AWS Kinesis

AWS Elastic Cache

AWS Redshift

AWS Lambda

Databricks

Delta Lake

Unity Catalog

Delta Live Tables

Terraform

Python

PySpark

Tools

Jenkins

GitHub Actions

Terraform

Job description

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