Architect Scalable Data Solutions: Design and implement data architectures optimized for machine learning workflows, including data ingestion, transformation, storage, and retrieval.
ML Infrastructure Design: Collaborate with data scientists and engineers to build infrastructure for training, testing, and deploying ML models at scale.
Data Governance & Quality: Establish data governance frameworks, ensure data integrity, and implement best practices for data security and compliance.
Cloud & On-Prem Integration: Develop hybrid data solutions that integrate cloud platforms (e.g., Azure, AWS, GCP) with on-premise systems.
Model Lifecycle Management: Support MLOps practices including versioning, monitoring, and retraining of models.
Stakeholder Collaboration: Work closely with business analysts, data scientists, and IT teams to align data architecture with business goals.
Performance Optimization: Tune data systems for performance, scalability, and cost-efficiency.
Requirements
Strong expertise in data modeling, ETL/ELT pipelines, and big data technologies (e.g., Spark, Hadoop).
Proficiency in cloud platforms (Azure) and containerization tools (Docker, Kubernetes).
Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps tools (MLflow, Kubeflow).
Deep understanding of data privacy, security, and regulatory compliance (e.g., GDPR, CCPA).
Excellent communication and stakeholder management skills.
Benefits
Competitive Salaries
Qualified Overtime
Paid Time Off (PTO)
Flexible Holiday Leave (88 hours per year)
Parental Leave
Immediate Healthcare: Medical, Dental, Vision, and Life Insurance
Employee Stock Ownership Plan (ESOP)
401(k) Retirement Plan (5% match on base compensation, immediate 100% vesting)
Tuition Reimbursement & Learning Allowance
Referral Bonus Program (up to $5k)
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data modelingETLELTbig data technologiesSparkHadoopML frameworksTensorFlowPyTorchScikit-learn