Jobs at ShyftLabs

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks using Python, PySpark, and SQL. Integrate data from databases, S3, files, and REST APIs; implement Medallion Architecture with Delta Lake; monitor, optimize, and ensure data quality and performance; collaborate with cross-functional teams to deliver production-ready data solutions.

Design, develop, and optimize big data solutions using Databricks, Apache Spark, and cloud platforms. Collaborate with teams to ensure high-performance data architectures.

Support day-to-day workplace operations including facility and supply management, front-desk and administrative tasks, meeting/event logistics, onboarding assistance, vendor coordination, and occasional local errands.

Lead AI and data initiatives, define strategy for data management, oversee evaluation frameworks, and align engineering and product teams for successful delivery.

The Technical Lead will design and implement backend systems, mentor engineers, optimize performance, and manage cloud architecture while ensuring scalable and reliable data processing.

Design and build scalable batch and streaming data pipelines, optimize ETL/ELT workflows and SQL workloads, manage ClickHouse clusters and large analytical databases, build ingestion from MongoDB/Postgres/Kafka/APIs, and implement monitoring and anomaly detection while partnering with Product, Analytics, and Backend teams.

The Senior AI Engineer will design and integrate AI features into backend systems, optimize models, and collaborate with teams for deployment and architecture best practices.

Responsible for managing HR initiatives, ensuring compliance, overseeing talent acquisition, and developing employee performance systems while fostering a positive work culture.

Design, build, and optimize Databricks-based big data pipelines and ETL workflows using Spark, SQL, and Python/Scala. Maintain data lakehouse architectures, enforce governance and security, automate CI/CD and IaC, monitor cluster performance, and collaborate with data teams to enable AI/ML workflows.

Lead end-to-end delivery of large enterprise cybersecurity programs, manage timelines, risks, dependencies, and stakeholders; drive Agile delivery as Scrum Master; partner with engineering, security, compliance teams; report program health and resolve cross-team roadblocks.

Responsible for bridging enterprise data with AI capabilities, translating business challenges into operational solutions, and driving complex sales deals by connecting technical solutions to business value.

Lead design, architecture, and delivery of enterprise-scale data platforms using Databricks and Spark. Architect Lakehouse solutions, build ETL/ELT pipelines, implement IaC and CI/CD, ensure data governance and security, mentor engineers, and partner with Fortune 500 clients from discovery through production.

The Senior Manager, Business Operations will lead contract management, support financial operations, ensure compliance, and optimize internal processes while enhancing client relations and business strategy execution.

The Technical Architect develops scalable platforms and systems, collaborates with product teams, and leads architecture across data solutions and integrations, ensuring best practices and mentoring developers.

The Associate AI Engineer will assist in building AI-powered backend services, integrating APIs, and developing data pipelines while collaborating with experienced engineers and teams.

The Operations Manager will oversee administrative operations, support talent acquisition, manage vendor relationships, and ensure efficient office functioning while aligning with company goals.

Design, build, and scale data-driven platforms and systems, focusing on backend and frontend engineering, integrations, and AI capabilities.

The Tech Lead/Solution Architect will design and deliver cloud solutions, lead architecture reviews, coordinate teams, and engage with clients to execute data and application projects.

The DevOps Engineer will optimize cloud computing solutions, enhance infrastructure performance, ensure high availability, and manage CI/CD tools while collaborating with development teams.

Develop and maintain ETL pipelines, manage data streaming solutions, optimize performance, and translate business requirements into scalable data systems.