Enterprise Technology
4 days ago
Our Company
At Teradata, we believe that people thrive when empowered with better information. That's why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers' customers—to make better, more confident decisions. The world's top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.
What You'll Do
Design, build, and optimize data pipelines to support AI/ML and analytics use cases across enterprise platforms (CRM, ERP, GCS, Finance).
Collaborate with Data Scientists to prepare and transform datasets for model training, validation, and deployment.
Implement ETL/ELT processes for structured and unstructured data from multiple enterprise systems (Salesforce, ServiceNow, ERP, etc.).
Build scalable data storage and processing solutions leveraging cloud platforms (Azure, Teradata Vantage).
Deploy and operationalize ML models into production, ensuring performance, monitoring, and retraining.
Apply MLOps practices (CI/CD for ML) to streamline deployment, versioning, and governance of models.
Ensure compliance with data security, privacy, and governance frameworks (GDPR, FedRAMP, SOX).
Continuously evaluate new tools and frameworks to improve data quality, model performance, and pipeline efficiency.
Who You'll Work With
Data Scientists – Collaborating to provide clean, usable data and production-ready ML pipelines.
Product Owners & Business Analysts – Understanding business requirements and aligning data/ML solutions with enterprise needs.
Engineering & QA Teams – Integrating ML outputs into SaaS platforms and validating end-to-end workflows.
Cloud & Security Teams – Ensuring data/ML pipelines are secure, compliant, and scalable.
Enterprise and Product Analytics Practices – Driving alignment with enterprise AI/ML strategies and best practices.
What Makes You a Qualified Candidate
3–6 years of experience as a Data Engineer or ML Engineer, preferably in SaaS/enterprise environments.
Strong programming skills in Python, SQL, and Scala/Java.
Experience with data processing frameworks (Spark, PySpark, Hadoop, Kafka, Airflow).
Proficiency with cloud data platforms (Teradata Vantage, AWS Redshift, Snowflake, BigQuery..).
Hands-on experience with ML deployment tools (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).
Familiarity with APIs, microservices, and integrating ML into enterprise workflows.
Strong knowledge of database design, data modeling, and performance optimization.
Preferred: Experience supporting enterprise SaaS platforms (Salesforce AI, ServiceNow AI, ERP).
What You'll Bring
A data-driven mindset and strong problem-solving skills.
Curiosity and adaptability—keeping pace with rapidly evolving AI/ML and data engineering tools.
Ability to collaborate across global, cross-functional teams.
Strong debugging and optimization skills for large-scale data systems.
A commitment to responsible data practices (data quality, privacy, governance).
Ownership mentality—ensuring data pipelines and ML workflows deliver business value.
LI-NM1-
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