We are seeking a Text Analytics Engineer (Conversational Data Pipelines & Analytics) to join our Text Analytics team. This role will focus on building and maintaining scalable solutions for processing, sampling, and analyzing customer conversations and unstructured text data. The engineer will develop data pipelines and analytics workflows that support conversational insights, prompt training data preparation, and downstream AI and machine learning use cases. This position will collaborate closely with the Text Analytics team as well as global Data Science and Engineering teams to build reliable pipelines for high-volume conversational data. Location : Mexico 100% Remote . Working hours are based on the US Pacific Time Zone. About Us: Abstra is a fast-growing, Nearshore Tech Talent services company, providing top Latin American tech talent to U.S. companies and beyond. Founded by U.S.-bred engineers with over 15 years of experience, Abstra specializes in sourcing skilled professionals across a wide range of technologies to meet our clients’ needs, driving innovation and efficiency. Responsibilities As a Text Analytics Engineer, you will design, build and maintain scalable data pipelines for conversational and text-based datasets. Develop data ingestion, transformation, enrichment, sampling and processing workflows for large-scale conversational data. Build and support Athena Studio workflows and related data processing pipelines for Text Analytics use cases. Create and implement sampling methodologies to support high-volume conversational data analysis, including sampling within conversations and across conversations. Prepare and process conversational data for analytics, prompt training, machine learning and downstream AI applications. Collaborate closely with the Text Analytics team in Argentina to align pipeline design, data quality and delivery priorities. Partner with Data Science, Engineering and Product teams to deliver reliable data flows that support conversational analytics capabilities. Develop validation, monitoring and quality-control checks for production data pipelines. Work with structured and unstructured datasets while ensuring performance, scalability and maintainability. Document pipeline logic, data transformations and operational processes clearly for technical stakeholders. Minimum Qualifications Bachelor’s degree in Computer Science, Engineering, Information Technology, Data Science or related technical discipline, or equivalent practical experience. 5+ years of relevant work experience in Data Engineering, Analytics Engineering, Text Analytics, Data Science or related technical areas. Hands-on experience building and supporting data pipelines and ETL/ELT workflows. Demonstrated proficiency in Python and SQL. Experience working with large-scale structured and unstructured datasets. Experience processing text data, conversational data, customer interaction data or similar unstructured data sources. Working knowledge of data quality, validation, monitoring and troubleshooting practices for data pipelines. Ability to collaborate effectively with cross-functional and geographically distributed teams. Preferred Qualifications Experience with Athena Studio and AWS Athena for analytics or data processing workflows. Experience with AWS data services such as S3, Glue, Lambda, EMR, SageMaker or similar cloud-based platforms. Experience with Spark, PySpark, Databricks or distributed data processing technologies. Experience developing sampling methodologies for analytics, NLP, prompt training or machine learning data preparation. Familiarity with Natural Language Processing, Text Analytics, Conversational Analytics or customer experience analytics domains. Familiarity with Generative AI, Large Language Models, prompt engineering concepts or LLM data preparation workflows. Strong debugging, performance optimisation and data pipeline reliability skills. Strong communication skills, especially in describing data workflows, pipeline behaviour and technical trade-offs to technical and non-technical audiences. Experience working with teams across India, Argentina or other global delivery locations. What We Offer Flexible working hours and hybrid remote work options. Opportunities for professional growth and development. A collaborative and inclusive work environment. The chance to work on impactful projects with a talented team. Excellent compensation in USD. Hardware and software setup (mandatory). Pre-Employment Verification As part of our standard onboarding process, candidates who successfully complete the interview process and accept an employment offer will be required to complete an employment verification check, and background check. This process will confirm job titles and dates of employment with two previous employers and is a standard requirement for all new employees joining the company.
SAP Analytics Engineer (Datasphere)
Capgemini Insurance
FBS - Associate Analytics Engineer
Capgemini Insurance
Analytics Engineer II (Contract)
Spring Financial Inc.
Lead Analytics Engineer
Sequoiaconnect
Analytics Engineering Manager
Sequoiaconnect
Cloud Data Analytics Engineer
Dotmatics