Senior Google Data Architect designing and delivering scalable data solutions on Google Cloud Platform. Collaborating across teams to shape target - state data architectures and influence enterprise data strategy.
About the role
- Data Engineer developing scalable data lake solutions and optimizing data pipelines at U.S. Bank. Collaborating with teams to manage data governance and cloud migration activities.
Responsibilities
- Design and implement scalable data lake solutions using Snowflake and Databricks
- Develop and optimize data pipelines for ingestion, transformation, and storage
- Manage data governance, quality, and security across cloud environments and implement performance tuning, automation, and CI/CD for data workflows
- Collaborate with cross-functional teams to support cloud migration activities
- Tune Hadoop, Hive, and Spark jobs and configurations for optimal performance, efficiency, and resource utilization
- Diagnose and resolve issues related to Linux servers, networks, cluster health, job failures, and performance bottlenecks
- Provide on-call support and collaborate with other teams to ensure smooth operations
- Implement and manage security measures within the Cloudera environment, including Kerberos, Apache Ranger, and Atlas, to ensure data governance and compliance
- Setup and manage HashiCorp Vault for secure keys and secrets management
- Migrate Datastage ETL jobs to Azure cloud services such as Azure Synapse Analytics, Azure Databricks, or Snowflake
- Develop scripts (e.g., shell, Ansible, Python) for automating administrative tasks, deployments, and monitoring
- Create and maintain documentation for system configurations, operational procedures, and troubleshooting knowledge bases
- Work closely with the vendor to stay current with the latest releases, perform upgrades, and address vulnerabilities
Requirements
- Bachelor’s degree, or equivalent work experience
- Three to five years of relevant experience
- Deep expertise in Data Engineering and Management technologies, synthetic data, automation, advanced analytics
- Ability to do on-call rotation once a month
- Very strong customer-centric focus
- 6 - 8 years of hands-on experience in Data engineering, Cloud platform management, and performance optimization
- Very strong Azure Data Factory tools experience
- Excellent SQL Experience, including performance tuning and optimization
- Hands-on experience with Hadoop, Hive, Spark, and migration of Big Data into Azure cloud services
- DataStage experience for conversion of ETL jobs to Pyspark ETL pipelines
- Working with offshore teams
- Working knowledge and hands-on experience in Data Integration and Data Lake Architectures with Databricks and Snowflake platforms
- Working knowledge of Microsoft Azure cloud and big data migration to cloud platforms
- Proficiency in Linux, clustering, and distributed systems
- Expertise in Hive and Spark for data processing and analytics
- Expertise in Hadoop ecosystem components such as HDFS, YARN, Hive, Spark, and Sqoop
- Proficiency in languages such as shell, Python, Pyspark for automating workflows, deployments, and monitoring
- Expertise in Linux, Network, Python scripting, DNS, Kerberos, LDAP/AD, JupyterHub
- Experience in creating and maintaining documentation for system configurations, operational procedures, and troubleshooting knowledge bases
- Strong problem-solving skills and the ability to diagnose and resolve system failures and performance bottlenecks
- Excellent communication and collaboration skills to work effectively with cross-functional teams
Benefits
- Healthcare (medical, dental, vision)
- Basic term and optional term life insurance
- Short-term and long-term disability
- Pregnancy disability and parental leave
- 401(k) and employer-funded retirement plan
- Paid vacation (from two to five weeks depending on salary grade and tenure)
- Up to 11 paid holiday opportunities
- Adoption assistance
- Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law
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