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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer IV - Customer Experience & AI Enablement - **Company:** The Itd - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Adobe Analytics, Agile Methodology, Artificial Intelligence, Airflow, Bash Shell, Big Data, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Systems, Data Visualization, Data Warehousing, Database Queries, Digital Assets, Dimensional Modeling, Google Analytics, Python (Programming Language), Machine Learning, Meta-Data Management, Performance Tuning, Query Optimization, Salesforce.Com, Software Engineering, SQL Databases, Data Streaming, Tableau (Software), Technical Data Management Systems, Web Analytics, Freeform SQL, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Generative AI, Git, Information Technology, Data Lineage, Data Analytics, Star Schema, Apache Kafka, Spark Streaming, Data Management, Tools for Reporting, Virtual Agents, Looker Analytics, Data Pipelines - **Published:** July 8, 2026 - **Apply:** https://job-boards.greenhouse.io/itd/jobs/4307407009 ## About the Role itD is seeking a Data Engineer IV - Customer Experience & AI Enablement to build scalable data infrastructure, self-service analytics capabilities, and AI-enabled data solutions that support global customer experience and post-sales operations. The ideal candidate will bring deep experience in data engineering, large-scale ETL/ELT pipeline development, workflow orchestration, and analytics enablement, with a track record of delivering reliable data platforms, interactive dashboards, and automation solutions that accelerate business insights and operational efficiency., * 6+ years of experience in data engineering, quantitative analytics, operational analytics, or related technical data roles. * Strong proficiency in SQL, including complex queries, query optimization, performance tuning, and window functions. * Strong Python programming experience for data engineering, automation, and large-scale data processing. * Demonstrated experience designing and building production-grade ETL/ELT pipelines and scalable data integration workflows. * Experience with workflow orchestration tools such as Apache Airflow or equivalent data pipeline orchestration technologies. * Experience with large-scale data processing and data warehousing technologies such as Apache Spark, Hive, Presto, Snowflake, or BigQuery. * Hands-on experience building self-service dashboards and data visualizations using Tableau, Looker, or equivalent business intelligence platforms. * Experience developing data models and applying dimensional modeling concepts, including star and snowflake schemas. * Demonstrated experience implementing data quality frameworks, validation processes, monitoring, alerting, and data governance standards. * Experience manipulating large datasets to generate actionable insights and deliver scalable data solutions. * Ability to independently manage multiple technical projects, navigate ambiguity, and deliver data solutions in a fast-paced environment. * Experience communicating complex technical and data concepts to both technical and non-technical stakeholders. * Bachelor's degree in Computer Science or a related technical field. Preferred Qualifications and Skills * Hands-on experience with Generative AI technologies, large language models, or AI-enabled analytics and automation solutions. * Experience with prompt engineering, retrieval-augmented generation architectures, LLM APIs, or AI agent workflows. * Experience building feature pipelines, curated datasets, or model-ready data assets supporting machine learning and AI use cases. * Experience integrating AI or machine learning outputs into dashboards, reporting tools, or operational workflows. * Experience with streaming data technologies such as Kafka or Spark Streaming. * Familiarity with Git, CI/CD practices for data pipelines, and infrastructure-as-code methodologies. * Knowledge of metadata management, data cataloging, and data lineage technologies. * Experience with customer experience, customer support, contact center, or post-sales operations data and metrics. * Experience working with customer support or CRM platforms such as Salesforce. * Experience with digital analytics platforms such as Google Analytics or Adobe Analytics. * Experience using Python or Bash scripting to automate workflows and build internal data tooling. * Familiarity with Agile development methodologies. * Experience working in fast-paced technology, startup, consumer electronics, or high-volume consumer product environments. * Prior contingent workforce or contractor experience supporting large-scale technology organizations. Education * Bachelor's degree in Computer Science or a related technical field required. ## Description Build and maintain scalable batch and streaming ETL/ELT pipelines, data models, and data warehouse architectures. Implement orchestration, quality, governance, and monitoring. Create self-service dashboards, enable ML/AI-ready datasets and feature pipelines, optimize performance, and collaborate with cross-functional teams to deliver analytics and AI-enabled solutions for customer experience and post-sales operations., * Design, develop, integrate, and maintain scalable batch and streaming data pipelines that support customer experience, customer support, survey, and digital support analytics use cases. * Build and optimize production-grade ETL/ELT workflows, data models, and data warehouse architectures to enable efficient, reliable, and scalable analytics. * Develop and maintain workflow orchestration processes for pipeline scheduling, dependency management, monitoring, and operational reliability. * Create interactive self-service dashboards and data visualizations that provide stakeholders with visibility into customer experience trends, operational metrics, and key performance indicators. * Implement data quality, validation, monitoring, alerting, governance, and lineage practices to ensure the accuracy, reliability, and usability of enterprise data assets. * Enable AI and machine learning analytics by developing curated datasets, feature pipelines, model-ready data assets, and AI-assisted analytical workflows. * Leverage large language models and generative AI capabilities to automate data workflows, accelerate insight generation, and reduce manual operational effort. * Partner with analysts, data scientists, engineering teams, customer support, and operations stakeholders to translate data requirements into scalable technical solutions. * Optimize complex SQL queries, data pipelines, storage utilization, and large-scale data processing workflows to improve performance and efficiency. * Champion data literacy and AI enablement through technical documentation, training, best-practice development, and knowledge sharing across cross-functional teams. Internal Responsibilities * Attend regular internal practice community meetings. * Collaborate with your itD practice team on industry thought leadership. * Complete client case studies and learning material, including blogs and media material. * Build out material to contribute to the Digital Transformation practice. * Attend internal itD networking events, both in person and virtual. * Work with leadership on career fast-track opportunities. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)