Data Engineer IV (6226)

Data Engineer IV – Customer Experience & AI Enablement

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.

 

This is a US-Remote opportunity, with candidates in the PST time zone preferred.

Duration: 12 months

We provide comprehensive medical benefits, a 401k plan, paid holidays, and more.
Please note that we are only considering direct W2 candidates at this time, as we are unable to offer sponsorship.

Responsibilities

• 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.

Required Qualifications and Skills

• 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.

Data Engineer IV – Customer Experience & AI Enablement

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.

 

This is a US-Remote opportunity, with candidates in the PST time zone preferred.

Duration: 12 months

We provide comprehensive medical benefits, a 401k plan, paid holidays, and more.
Please note that we are only considering direct W2 candidates at this time, as we are unable to offer sponsorship.

Responsibilities

• 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.

Required Qualifications and Skills

• 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.