Maximize Your Impact with TP
Welcome to TP, a global hub of innovation and empowerment, where we redefine the future. With a remarkable 10 billion annual revenue and a global team of 500,000 employees serving 170 countries in over 300 languages, we lead in intelligent, digital-first solutions.

As a Great Place to Work certified in 72 countries, our culture thrives on diversity, equity, and inclusion. We value your unique perspective and believe that your talent is the missing piece that completes our vision for a brighter, digitally driven tomorrow.

The Opportunity
We are seeking a hands-on Senior Data Engineer - AI to design, build, and operate the data foundations that power TPs analytics, automation, and AI solutions across client and internal operations. The role covers the full data lifecycle - acquisition, integration, storage, processing, quality, security, and production observability - transforming high-volume structured and unstructured data into trusted, reusable, model-ready assets.

This professional will work with Operations, clients, Product, Consulting, Data Science, Architecture, and Software Engineering teams across LATAM and global functions. The ideal candidate combines deep technical expertise, business understanding, client-facing communication, and technical leadership to deliver scalable, resilient, secure, and cost-efficient data products in multi-client cloud environments.

The Responsibilities

- Architect, build, and operate scalable batch and real-time data pipelines for high-volume analytics, automation, machine learning, and Generative AI workloads.
- Acquire and integrate structured and unstructured data from CRM, contact-center, WFM, QA, HR and recruitment systems, APIs, files, event streams, transcripts, and client platforms.
- Design cloud data lake, lakehouse, warehouse, data mart, and semantic-layer architectures aligned with operational, analytical, and client requirements.
- Develop reusable data products and standardized ingestion, transformation, and validation components that can be applied across countries, clients, and solutions.
- Prepare trusted, model-ready datasets for ML, NLP, speech, forecasting, predictive analytics, and Generative AI use cases.
- Build and maintain feature pipelines, feature stores, embeddings, vector indexes, retrieval workflows, and evaluation datasets for RAG and other AI solutions.
- Partner with Data Scientists and AI Engineers to improve data relevance, freshness, traceability, and reproducibility throughout the model lifecycle.
- Establish data-quality rules, lineage, metadata, observability, data contracts, and measurable service levels across the data lifecycle.
- Enforce PII protection, client-data isolation, retention policies, encryption,



role-based access controls, and applicable regulatory and contractual requirements.
- Support auditability and governance standards in multi-client environments, ensuring data is used only for authorized business and AI purposes.
- Ensure production reliability and 24x7 operational readiness through monitoring, alerting, incident response, root-cause analysis, and preventive improvements.
- Optimize pipeline performance, storage, compute utilization, and cloud cost while preserving scalability, resilience, and service commitments.
- Define backup, recovery, continuity, and disaster-recovery practices for critical data services.
- Translate operational and client needs into solution designs, technical estimates, delivery plans, proofs of concept, and production-ready implementations.
- Collaborate with Operations, Product, Consulting, Architecture, Security, Data Science, BI, and Software Engineering teams to deliver end-to-end solutions.
- Produce clear technical documentation, participate in architecture reviews, lead code reviews, and promote engineering standards and reusable patterns.
- Mentor engineers, provide technical guidance, and contribute to the continuous improvement of TP data and AI capabilities.

The Qualifications

- Bachelors degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related field.
- 7+ years of data engineering experience, including at least 2 years supporting production AI/ML, NLP, speech, or Generative AI workloads.
- Demonstrated experience delivering enterprise data platforms in high-volume, multi-client, regulated, or 24x7 operational environments.
- Postgraduate studies or relevant certifications in Data, AI, Cloud, Databricks, Snowflake, AWS, Azure, or GCP are desirable.
- Advanced SQL and Python, with strong software-engineering, automated-testing, and code-quality practices.
- ETL/ELT and orchestration using tools such as Airflow, Azure Data Factory, AWS Glue, dbt, or equivalent technologies.
- Distributed processing with Spark or Databricks, and cloud data warehouses such as Snowflake, BigQuery, Redshift, or Synapse.
- Data modeling across relational, NoSQL, object-storage, lakehouse, analytical, and data-product patterns.
- Streaming and integration technologies such as Kafka, Kinesis, Event Hubs, Pub/Sub, APIs, and event-driven architectures.




- AI data patterns including feature stores, unstructured data processing, embeddings, vector databases, RAG, and model-evaluation datasets.
- AS, Azure, or GCP architecture, including security, networking, monitoring, reliability, and cost optimization.
- Engineering and governance practices including Git, CI/CD, containers, Infrastructure as Code, cataloging, quality, lineage, privacy, and compliance.
- Experience with contact-center, BPO, customer-experience, workforce, quality, recruitment, or operational data is highly desirable.
- Experience integrating client platforms and enterprise systems such as CRM, WFM, HRIS, speech analytics, telephony, and cloud contact-center solutions is a plus.
- Understanding of multi-tenant or multi-client architectures and strict data-segregation requirements is strongly preferred.
- Advanced problem-solving, troubleshooting, and root-cause analysis.
- Strong architectural thinking and the ability to balance business value, risk, scalability, performance, and cost.
- Clear communication with technical, operational, business, and client stakeholders.
- Hands-on technical leadership, coaching, mentoring, and constructive code-review capabilities.
- Ability to manage multiple priorities, dependencies, risks, SLAs, and delivery commitments.
- Collaboration across multidisciplinary, multicultural, and geographically distributed teams.
- Ownership, autonomy, sound decision-making, adaptability, and a results-oriented mindset.
- Continuous learning, attention to detail, and commitment to operational excellence.

Pre-Employment Screenings
By TP policy, employment in this position will be contingent on your successful completion of and passage of a comprehensive background check, including global sanctions and watchlist screening.

Significant | Policy on Unsolicited Third-Party Candidate Submissions
TP does not accept candidate submissions from unsolicited third parties, including recruiters or headhunters. Applications will not be considered, and no contractual association will be established through such submissions.

Culture Belonging
At TP, we are committed to fostering a diverse, equitable, and inclusive workplace. We welcome individuals from all backgrounds and lifestyles and do not discriminate on the basis of gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or other differences.

#LI-AS1 #LI-Remote

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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