ShyftLabs

• Drive architectural decisions for AI applications, enterprise integrations, and cloud-agnostic deployments across AWS, Azure, and GCP environments • Collaborate with Technical Leads on system design, database optimization, API architecture, and scalable backend/frontend development • Lead, mentor, and grow a team of 7-12 software engineers by providing technical guidance and structured career development • Stay current with AI/ML technologies, agentic AI frameworks, and enterprise integration patterns • Partner with Product Managers to translate business requirements into technical roadmaps and deliverable timelines • Implement agile development processes, engineering best practices, and continuous improvement initiatives • Coordinate with cross-functional teams including data, DevOps, and customer success teams • Establish code quality standards, testing frameworks, and deployment pipelines

• Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer. • Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing. • Develop and manage our central data warehouse in Google BigQuery. • Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability. • Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets. • Build reliable transformation workflows that support analytics, reporting, and data science initiatives. • Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency. • Implement BigQuery best practices, including partitioning, clustering, query optimization, and materialized views. • Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting. • Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms. • Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems. • Establish processes for data governance, observability, and lineage tracking. • Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products. • Lead and support client and stakeholder communication, working with enterprise clients to translate business needs into scalable data solutions. • Partner with product teams and leadership to ensure that technical data solutions align with business strategy and client expectations. • Take ownership of data platforms and architecture decisions, helping shape the future direction of our analytics and data infrastructure. • Identify opportunities to improve data reliability, automate workflows, and generate new insights through data. • Contribute to a collaborative, high-performing engineering culture with strong communication and teamwork.