• Work with limited supervision to support data engineering and data analytics solutions. Provide design guidance and implementation for end-to-end analytic solutions. Coordinate with SWA focused groups to create unique data infrastructure, run tests on their designs to isolate errors and update systems to accommodate changes in company needs

• Assemble large, complex sets of data that meet non-functional and functional business requirements
• Identify, design and implement internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Build required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies
• Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition
• Work with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues
• Work with stakeholders including the Executive, Product, Data and Design teams to support their data infrastructure needs while assisting with data-related technical issues
• Generate or adapt equipment and technology to serve user needs
• May perform other job duties as directed by Employee's Leaders
• Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
• Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
• Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
• Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
• Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
• Lead scalable enterprise data engineering foundations supporting Agent Observability, Ops Enablement, and future autonomous operations.
• Own telemetry ingestion, data processing pipelines, evaluation datasets, and reusable data engineering frameworks on AWS.
• Serve as the technical partner across Product, AI, Operations, and Engineering teams while improving platform reliability, governance, and scalability.

• Knowledge of the practical application of engineering science and technology, including applying principles, techniques, procedures, and equipment to the design and production of various goods and services
• Knowledge of design techniques, tools, and principles involved in production of precision technical plans, blueprints, drawings, and models
• Ability to use logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems
• Ability to understand new information for both current and future problem-solving and decision-making
• Skilled in identifying complex problems and reviewing related information to develop and evaluate options and implement solutions
• Ability to recognize when an issue has occurred or is likely to occur, without needing to diagnose or resolve the problem.
• Ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated events)
• Ability to switch efficiently between multiple tasks or information sources, such as spoken instructions, system alerts, or data inputs.
• Ability to organize data or actions in a defined sequence or structure based on specified rules or patterns (e.g., numerical, textual, visual, or mathematical sequences).
• Ability to recognize defined patterns—such as shapes, words, or signals—even when they are embedded within distracting or complex information.
• Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
• Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
• Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
• Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
• Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
• Databricks and Apache Spark
• Iceberg, Delta Lake, and Hudi
• Kafka, Kinesis, and streaming architectures
• AI/GenAI telemetry, vector databases, RAG analytics, QuickSight, or Grafana

• Required: Bachelor's degree in Computer Science, Engineering, Information Systems or related field and/or equivalent formal training

• Required: Intermediate level experience, fully functioning broad knowledge in:
  • Cloud infrastructure, DataLake
  • ETL experience ensuring source to target data integrity
  • Various filetypes (Delimited Text, Fixed Width, XML, JSON, Parque).
  • ServiceBus, setting up ingress and egress within a subscription, or relevant AWS Cloud services administrative experience.
  • Unit Testing, Code Quality tools, CI/CD Technologies, Security and Container Technologies
  • Agile development experience and Agile ceremonies and practices 
  • 2-5 years of relevant work-related experience 
  • Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
  • Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
  • Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
  • Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
  • Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership

Preferred Experience

• Databricks and Apache Spark
• Iceberg, Delta Lake, and Hudi
• Kafka, Kinesis, and streaming architectures
• AI/GenAI telemetry, vector databases, RAG analytics, QuickSight, or Grafana

• Must meet confidentiality expectations as to confidential, proprietary and sensitive Company information
• Ability to work extended hours as needed
• Ability to work onsite approximately 3 days per week and as required by the business.