Technical Program Manager
GroqSalary not specified
Full Time
Senior (5 to 8 years), Expert & Leadership (9+ years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Technology, DataIndustries
Requirements
- 5 to 10 years of engineering experience, ideally in fast-paced environments, with a mix of hands-on technical work and leadership
- Strong backend and cloud expertise across GCP/AWS, distributed systems, APIs, data workflows, and automation frameworks
- Experience leading complex engineering or platform teams working on AI, automation, or large-scale data products
- Comfortable diving deep into architecture and guiding engineers through trade-offs in reliability, scale, and cost
- A builder’s mindset, outcome-focused, pragmatic, and able to find elegant solutions without overengineering
- Excellent communication skills with the ability to distill technical concepts for both engineering teams and business stakeholders
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
- Bonus: Experience in startup or high-growth product environments where speed, adaptability, and ownership are essential
Responsibilities
- Lead the end-to-end engineering lifecycle, from problem definition and PRD creation to delivery, launch, and iterative improvement across backend, AI, and data-platform initiatives
- Translate business goals into detailed technical requirements, epics, and user stories for engineering teams working in Node.js, Python, React, and GCP/AWS cloud environments
- Partner with engineering, data science, DevOps, QA, and design to prioritize roadmap items aligned with OKRs, cost constraints, and delivery velocity
- Define and own success metrics including SLAs, SLOs, scalability, and cost-performance trade-offs while ensuring reliable and high-performing system delivery
- Lead the development of AI-driven automation initiatives, such as OCR pipelines, reconciliation engines, anomaly detection, and LLM-powered data extraction
- Conduct technical feasibility assessments and guide architecture decisions (e.g., GCP Cloud SQL vs. Aurora, Kafka vs. Pub/Sub, RDS Proxy adoption)
- Maintain alignment across teams through sprint planning, stakeholder reviews, backlog grooming, retrospectives, and transparent documentation
- Own engineering documentation standards including PRDs, SOWs, cost models, and architecture diagrams
- Coordinate with key vendors and partners (e.g., Altinity, AWS, Petco Cloud) to support integrations, platform scaling, and compliance readiness
Skills
Node.js
Python
React
GCP
AWS
Cloud SQL
Aurora
Kafka
Pub/Sub
RDS Proxy
AI
OCR
LLM
data extraction
DevOps
ShyftLabs
Data-driven decision-making solutions for organizations
About ShyftLabs
ShyftLabs helps organizations adopt a data-first approach to their decision-making processes. Their services focus on establishing systems that enable companies to make quicker and more informed decisions based on data analysis. This approach allows businesses to gain insights that can keep them ahead of their competitors. Unlike other companies that may offer generic consulting services, ShyftLabs emphasizes the importance of data in driving decisions, ensuring that organizations can leverage their data effectively to enhance their strategic planning and operational efficiency.
None, CanadaHeadquarters
2018Year Founded
VENTURE_UNKNOWNCompany Stage
Data & Analytics, ConsultingIndustries
11-50Employees
Benefits
Health Insurance
Hybrid Work Options
Professional Development Budget
Risks
Increased competition from startups offering innovative, cost-effective solutions.
Growing demand for in-house analytics teams reducing reliance on consultants.
Rapid AI advancements may outpace ShyftLabs' current technology offerings.
Differentiation
ShyftLabs specializes in data governance, warehousing, and predictive analysis services.
The firm empowers organizations with a data-first approach for decision-making.
ShyftLabs establishes processes for faster, insightful decisions to outpace competition.
Upsides
Increased demand for data governance due to stricter privacy regulations.
Growing interest in predictive analytics in retail for inventory optimization.
Rising adoption of cloud-based BI tools among SMEs for cost-effectiveness.
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