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Position: BigQuery Database Administrator (DBA)
Location: Chicago preferred; remote within the U.S. considered
Employment Type: 6-Month Contract-to-Hire
Work Authorization: U.S. Citizen or Green Card holder; no visa sponsorship
Overview
The BigQuery Database Administrator will take ownership of the performance, reliability, security, and operational effectiveness of enterprise data environments built on Google BigQuery and Google Cloud Platform (GCP). This person will administer and optimize BigQuery workloads, troubleshoot performance issues, strengthen monitoring and access controls, and help ensure enterprise data remains available, secure, scalable, and cost-efficient. The DBA will also play an important role in data-platform modernization, supporting migrations from legacy database environments into modern cloud-based architectures and validating data throughout the transition. Working closely with data engineers, architects, application teams, and business stakeholders, this person will support integrations using Talend or comparable ETL technologies and recommend practical cloud data best practices. Success requires someone who can move beyond reactive database support to proactively identify risks, optimize the environment, and help build a more scalable and reliable cloud data platform.
Proposition
Purpose: Take ownership of a strategically important BigQuery environment and help transform enterprise data infrastructure into a secure, scalable, high-performing cloud platform. The work will directly influence the reliability, performance, cost efficiency, and modernization of data used across the business.
Growth: Expand beyond traditional database administration into modern cloud data architecture, BigQuery optimization, GCP services, enterprise data integration, governance, and large-scale modernization. The role provides exposure to complex migration and transformation initiatives while working closely with engineering and architecture teams.
Motivators: This opportunity is suited to someone who enjoys solving difficult database and performance problems, improving complex environments, and seeing measurable results from technical decisions. The person will have meaningful ownership, opportunities to establish best practices, and the ability to influence how the organization operates and scales its cloud data platform.
Objectives
1. Stabilize and Establish Operational Control of the BigQuery Environment. Within the first 60 days, assess the existing BigQuery and supporting GCP environment and establish a clear baseline for performance, reliability, security, access, workload behavior, and operational risks. Identify critical issues involving queries, capacity, data structures, permissions, monitoring, availability, or support processes and implement prioritized improvements. Establish proactive monitoring and repeatable troubleshooting practices that allow problems to be identified and resolved before materially affecting users or downstream systems. Success will be measured by improved environment stability, visibility into critical workloads, timely resolution of priority issues, documented operating practices, and reduced recurring incidents.
2. Improve BigQuery Performance, Scalability, and Cost Efficiency. Within the first 3 months, analyze high-impact BigQuery workloads, SQL queries, data structures, usage patterns, and resource consumption to identify opportunities for measurable optimization. Implement appropriate improvements involving query design, partitioning, clustering, data organization, workload management, or other BigQuery optimization techniques while balancing performance, scalability, reliability, and cost. Partner with engineering teams to prevent inefficient patterns from being repeatedly introduced. Success will be measured through documented improvements in priority workload performance, resource efficiency, query reliability, and cost visibility, with baseline and post-improvement results used where data is available. AI-assisted SQL analysis and optimization tools may be used when recommendations are independently validated.
3. Support Successful Data Modernization and Cloud Migration. During the first 3 months and throughout modernization initiatives, support the migration of legacy database workloads and data into BigQuery and related GCP services. Assess source environments, support migration planning and transformation activities, identify compatibility and data-quality risks, and validate migrated data for completeness, accuracy, and expected performance. Work with Talend or comparable ETL/data integration technologies to support reliable ingestion, transformation, and integration processes. Success will be measured by accurate migrations, timely resolution of data or performance issues, validated source-to-target results, minimal disruption to dependent systems, and successful transition of workloads into supportable cloud environments.
4. Establish a Secure, Scalable BigQuery Operating Model. Over the first 3-6months, help establish repeatable standards and best practices for BigQuery administration, security, IAM, monitoring, performance management, data integration, troubleshooting, and change management. Collaborate with architects and data engineers to ensure new solutions are designed for scalability, maintainability, governance, and operational support rather than requiring repeated remediation after deployment. Document reusable practices and recommendations that improve consistency across the data platform. Success will be measured by stronger operational standards, improved security and governance controls, reduced recurring issues, better maintainability, and increased confidence in the platform's ability to support future growth.
Subtasks
1. Assess the BigQuery and GCP Environment. Within the first 30 days, review the current BigQuery architecture, workloads, data structures, SQL patterns, integrations, IAM configuration, monitoring, security controls, and operational processes. Identify immediate risks, performance bottlenecks, recurring incidents, and modernization dependencies and produce a prioritized improvement plan. Success will be measured by a validated baseline, clear priorities, and early resolution of critical operational issues.
2. Establish Proactive Monitoring and Troubleshooting. Within the first 60 days, strengthen monitoring of BigQuery workloads, query performance, failures, resource usage, integrations, and other critical indicators. Develop repeatable troubleshooting and root-cause practices and document recurring issues and resolutions. Success will be measured by faster issue identification and resolution, improved operational visibility, fewer repeated problems, and increased platform reliability.
3. Optimize SQL, Queries, and Data Structures. During the first 3 months, identify high-impact performance and cost issues and optimize SQL, query patterns, schemas, partitioning, clustering, and related BigQuery structures where appropriate. Work with developers and data engineers to improve inefficient designs and establish practical optimization guidelines. Success will be measured by documented improvements in priority query performance, reliability, scalability, or resource efficiency.
4. Strengthen Security, IAM, and Data Governance Controls. During the first 3-6 months, evaluate and improve access management, IAM, permissions, security controls, and relevant governance practices across BigQuery and supporting GCP services. Identify excessive or inappropriate access, support remediation, and ensure controls align with organizational security and compliance requirements. Success will be measured by stronger access controls, documented governance practices, timely remediation of identified risks, and successful compliance with applicable standards.
5. Execute Data Integration and Modernization Activities. Throughout the first 3 months, support ingestion, transformation, migration, and integration workflows using Talend or comparable ETL tools and relevant GCP services. Collaborate with engineers and architects to move legacy data and workloads into scalable cloud architectures while validating data accuracy and resolving transformation issues. Success will be measured by reliable pipelines, accurate migrated data, successful cutovers, and minimal disruption to dependent applications and users.
6. Establish DBA Best Practices and Cross-Team Collaboration. Over the first 3-6 months, document and promote standards for BigQuery administration, monitoring, SQL optimization, troubleshooting, security, migration, and operational support. Participate in architecture and technical discussions and provide practical recommendations to data engineers, architects, and application teams. Success will be measured by adoption of repeatable practices, reduced avoidable technical issues, improved cross-team execution, and a more maintainable enterprise data platform.
7. Continuously Evaluate and Integrate AI to Improve Performance. Within the first 45 days, identify how AI and automation can improve BigQuery administration, SQL optimization, monitoring, anomaly detection, troubleshooting, migration validation, documentation, and operational support. Evaluate approved AI-enabled tools and lead practical pilots where automation can reduce manual effort, accelerate root-cause analysis, or improve platform performance and reliability. Validate all AI-generated recommendations, scripts, SQL, and configurations before production use and maintain required security and governance controls. Success will be measured by demonstrated improvements in DBA productivity, issue resolution, optimization effectiveness, or platform reliability and by embedding responsible AI-assisted practices into ongoing operations.