• Support the Data Solutions- Operations team in a variety of functions, such as: • Support development and testing of configuration-driven ETL jobs in AWS Glue. • Assist in standardizing job patterns, schema contracts, and reusable utilities. • Help refactor legacy or hard-coded logic into config-driven structures. • Document pipeline dependencies, data flows, and operational runbooks. • Contribute to data quality checks, validation rules, and monitoring improvements. • Assist with troubleshooting job failures and performance optimization efforts. • Support CI/CD, deployment validation, and environment configuration consistency. • Perform additional duties as assigned or requested.

• Support master data and analytics initiatives by assisting with taxonomy development, data standardization, and enterprise data modeling. • Conduct taxonomy and classification research to support master and reference data modeling. • Assist in documenting business definitions, hierarchies, and data relationships. • Support data modeling and schema updates within the Nexxus project. • Contribute to data quality analysis and remediation efforts. • Help prepare datasets and transformations supporting master data workflows. • Collaborate with Data Solutions and business stakeholders to validate taxonomy structures. • Perform additional duties as assigned or requested.

• The Cloud Data Architect serves as the senior technical authority responsible for defining, governing, and evolving enterprise architecture to support mission and business objectives • Leads the development of architecture strategies, standards, and technical roadmaps • Ensures alignment with enterprise architecture frameworks and federal best practices • Provides architectural oversight across engineering, product development, infrastructure, data platforms, and digital services • Develops and maintains enterprise architecture framework, reference architectures, and technology standards • Establishes and leads architecture governance processes • Maintains enterprise architecture artifacts • Defines and maintains reference architectures for cloud environments, SaaS platforms, enterprise applications, and data services • Guides engineering and development teams in designing integrated, scalable, and resilient solutions • Develops and maintains multi-year technology roadmaps aligned with organizational goals

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a visionary and hands-on Data Engineering Lead to spearhead the design, development, and optimization of our next-generation data platform. As a Lead, you will balance technical excellence with people leadership, ensuring our data architecture is scalable, resilient, and perfectly aligned with our business goals while collaborating cross-functionally to support analytics, reporting, and operational data needs. The ideal candidate should be a PySpark expert who thrives in the Azure ecosystem and has a deep appreciation for clean, modular code and robust ETL patterns. This is an exciting opportunity to work along with a great team of data engineers, demanding technologies, and an engaging work environment to help shape our data engineering best practices. Key Responsibilities - Technical Leadership: Design and supervise the implementation of comprehensive data pipelines utilizing Azure Databricks and PySpark. - Team Mentorship: Direct a team of data engineers, performing code reviews, offering technical expertise, and cultivating a culture of ongoing learning. - Data Modeling & Optimization: Develop high-performance schemas in PostgreSQL and refine complex SQL queries for large datasets. - ETL Strategy: Establish and apply optimal practices for data ingestion, transformation, and storage (Delta Lake/Lakehouse patterns). - Strategic Collaboration: Collaborate closely with Data Analysts, Architects, and Product Managers to convert business requirements into technical specifications. - Process Improvement: Promote the implementation of CI/CD, unit testing, and automated monitoring to achieve 99.9% data reliability. - Ensure data quality, governance, and compliance through validation, documentation, and secure practices. - Continuously improve data systems for enhanced performance, reliability, and scalability. - Effectively engage within an agile, cross-functional project team. Mandatory Skillset - Azure Databricks: - Expert-level experience managing workspaces, clusters, and job scheduling. - Solid understanding of data lakehouse architectures and Delta Lake. - Proven experience in Performance Tuning, Spark Optimization and Cost Reduction. - PySpark: Advanced proficiency in Spark DataFrame APIs and Spark SQL for large-scale data processing involving various data formats. - SQL Mastery: Exceptional ability to write, tune, and troubleshoot complex queries. - PostgreSQL: Hands-on experience with relational database design, indexing, and performance optimization. - ETL/ELT Frameworks: Proven track record of building scalable data pipelines from scratch. Desired Skills - Workflow Orchestration: Experience with Apache Airflow for managing complex task dependencies. - Containerization: Familiarity with Azure Kubernetes Service (AKS) for deploying containerized data services. - Infrastructure as Code (IaC): Knowledge of Terraform or Bicep for managing Azure resources. Qualifications - 10+ years of experience in Data Engineering or Software Engineering. - 3+ years as a formal technical Lead managing an agile team and implementing E2E solutions. - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. - Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders. - Strong problem-solving skills and attention to detail. Benefits - You won't just be "maintaining" pipelines; you'll be the primary architect of a data ecosystem that powers real-time decision-making across the entire organization.