Data Engineer

• Design and build batch and streaming data pipelines on Azure and Databricks, with primary focus on real-time IoT and telemetry use cases within a Medallion architecture.
• Develop ETL/ELT workflows to ingest, transform, and validate large volumes of structured and unstructured data.
• Build and maintain data services, APIs, and microservices for application, analytics, and ML/AI teams.
• Implement real-time streaming solutions using Azure Event Hubs, Azure Stream Analytics, and related Azure integration patterns, with cost-effective throughput, partitioning, and downstream delivery to Databricks.
• Optimize production Databricks pipelines using PySpark, Spark SQL, and Delta Lake, including Spark tuning for performance, reliability, and cost.
• Troubleshoot and resolve complex pipeline issues across Databricks, Azure, and on-premises systems, including root cause analysis and corrective action.
• Partner with data analysts, software engineers, ML engineers, and business stakeholders to translate requirements into technical designs and delivery priorities.
• Apply data quality, validation, and privacy-first practices, and deliver reliable pipelines through software engineering standards, documentation, testing, and CI/CD.

Senior Data Engineer

• Lead a team of data engineers to build and operate scalable, reliable data pipelines and platforms
• Drive engineering excellence (code quality, CI/CD, testing, observability, incident management)
• Partner with product and analytics to prioritize and deliver high-impact data use cases
• Own production support, incident response, and continuous improvement of data systems
• Establish and scale DataOps practices across development and operations
• Collaborate across AWMT D&A to enable data products that support personalization and analytics
• Hire, develop, and grow engineering talent and future tech leads
• End-to-end delivery and operation of data pipelines and platforms supporting advice products
• Engineering quality, reliability, and standardization across your team
• Talent development for data engineers and tech leads
• Cross-team alignment with product, analytics, and ML engineering
• Improving reliability and observability of production data pipelines
• Driving adoption of consistent pipeline frameworks and engineering standards
• Enabling scalable data foundations for ML and GenAI use cases
• Strengthening team execution, prioritization, and stakeholder trust

Manager, Data Engineering

• Own the data layer of the Fabric platform
• Partner closely with AI Engineers on AI-ready data products
• Design and implement scalable data storage in OneLake
• Build and maintain data ingestion pipelines
• Manage the lifecycle of Fabric data components
• Optimize Lakehouse tables for performance
• Define and operate the on-call model for production data pipelines

Fabric Data Engineer – Workplace Engineering

• Design and deliver scalable data solutions that power decision-making across the organization.
• Take ownership of key components of the data platform, working hands-on with modern tools and technologies to build reliable, high-quality data pipelines.
• Collaborate with teams across the business, contribute to architectural decisions, and support the growth of other engineers through mentorship and knowledge sharing.
• Solve complex data challenges through clean, maintainable code.

• Work closely with experienced engineers and analysts to build reliable data pipelines
• Shape how data flows across the organization
• Writing code daily
• Solving real data challenges
• Contributing to scalable data solutions

Associate Data Engineer

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC, SFMC, 3rd Party SaaS, and SAP
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Build audience definitions and activation paths for on-site personalization, SFMC campaigns, CRM actions, and downstream systems
• Produce runbooks, onboarding guides, and self-service tooling for marketers, CRM owners, and platform teams.
• Lead cross-functional teams to deliver CDP capabilities.

Customer Data Platform Lead

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC (CRM), SFMC (Marketing Automation), 3rd Party SaaS, and SAP (ERP)
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Define the CDP vision, roadmap, and prioritized use cases aligned to commercial and digital objectives
• Establish canonical data models, event taxonomy, identity graphs, and profile schemas required for personalization and measurement
• Design robust data capture from Sitecore AI DXP, web/mobile events, product catalogs, clinical interactions, and other touchpoints
• Manage Sitecore CDP relationships and third-party integration partners

• Design, develop, and execute enterprise CRM data migration solutions using KingswaySoft and Microsoft SQL Server Integration Services (SSIS).
• Perform data extraction from legacy SQL databases and other source systems.
• Develop ETL processes for transforming, cleansing, validating, and loading data into Microsoft Dynamics CRM/365.
• Analyze source system data structures and map data elements to Dynamics CRM entities and relationships.
• Develop and optimize SQL queries, stored procedures, views, and SSIS packages.
• Migrate both structured and unstructured data, including images, attachments, and correspondences.
• Support Dynamics CRM development activities related to integrations, plugins, workflows, and customizations.
• Conduct data quality assessments and remediation activities.
• Troubleshoot migration and performance issues across development, test, and production environments.
• Collaborate with business analysts, solution architects, developers, and project managers during implementation activities.
• Support mock migrations, validation testing, reconciliation, and production cutover activities.
• Document migration architecture, mappings, transformation logic, and operational procedures.
• Ensure compliance with organizational security and data governance standards.

Senior MS Dynamics CRM Data Migration Developer

• Lead the overall architecture, planning, and execution of SAP BRIM data migration initiatives
• Define migration strategy, frameworks, standards, governance, and best practices for SAP BRIM implementations
• Design end-to-end migration solutions for customers including contract accounts, subscriptions, billing, invoicing, payments, and financial data
• Assess legacy system data structures and map them to SAP BRIM target models
• Develop migration roadmaps, cutover strategies, and reconciliation approaches
• Support SAP BRIM modules including: SAP Convergent Charging (CC), SAP Convergent Invoicing (CI), SAP Subscription Order Management (SOM), SAP Contract Accounts Receivable and Payable (FI-CA), SAP Billing and Revenue Innovation Management integrations
• Perform data profiling, cleansing, transformation, validation, and reconciliation activities
• Define and implement data quality controls and governance processes
• Create source-to-target mapping documents and transformation rules
• Identify and resolve data inconsistencies and migration defects
• Provide leadership to migration developers, analysts, and technical teams
• Oversee development and execution of ETL processes, migration scripts, and automation tools
• Coordinate with SAP Basis, security, infrastructure, and integration teams
• Support unit testing, system integration testing, user acceptance testing, and mock conversions
• Lead reconciliation and validation activities to ensure migrated data accuracy and completeness
• Develop and execute migration cutover plans and support production deployment and hypercare activities
• Coordinate migration activities during go-live events and stabilization periods

SAP BRIM Data Migration Architect

• Build transformative AI capabilities from the ground up, including model integration and prompt engineering.
• Contribute to the technical direction for how we ingest, transform, store, serve, and govern the data that powers our LLM-based and agentic systems.
• Build real-time, user-facing AI features that directly shape business planning and decision-making.
• Contribute to the data architecture, design, and deployment of scalable Generative AI and Machine Learning systems into production environments.
• Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments.
• Integrate and optimize LLMs for specific business planning use cases, including prompt engineering and RAG implementation.
• Design and build the retrieval and knowledge layer powering our RAG and agentic workloads, such as vector databases, graph databases, knowledge graphs, hybrid search, and embedding pipelines.
• Help design the knowledge graph that captures the semantics of customer models, metrics, hierarchies, and relationships.
• Build the data plane for evaluation and continuous improvement, working with cutting-edge conversational and agentic AI technologies.
• Engineer the feature and context pipelines that feed forecasting and anomaly-detection models at customer scale, balancing batch and streaming patterns.
• Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics.

Senior Data Engineer – AI

• Build strong partnerships with SPD experimentalists, process engineers, and analytical scientists to gather requirements for data solutions which will have direct pipeline impact
• Design and implement robust, scalable data pipelines that ingest experimental and process data from SPD teams
• Deliver analysis-ready datasets to support SPD digital initiatives, including process modeling and Bayesian optimization
• Define and enforce data standards, metadata schemas, and ontologies that make SPD data interoperable and readily consumable by downstream modeling and optimization workflows
• Automate data ingestion from laboratory instruments, electronic lab notebooks, PAT systems, and manufacturing systems and integrate with cloud-based storage and compute environments
• Apply and generate data analysis and visualization workflows
• Design and develop dashboards, reports, and data exports
• Curate data and support definition of needs for automation of data ingestion
• Influence digital data strategy for SPD by identifying opportunities to improve data capture practices at the source and reduce friction between experimentation and modeling
• Demonstrate excellent interpersonal, communication, and collaboration skills
• Embrace and model our core values including fostering a supportive culture where all can thrive
• Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment
• Perform impactful scientific innovation in a team-oriented manner that builds trusted partnerships across vast stakeholder networks
• Publish and present research, including maintaining an established track record of interaction with the broader academic community

Associate Principal Scientist, Data Engineer, Digital Insights

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Assist in developing and executing data migration solutions using KingswaySoft and Microsoft SQL Server Integration Services (SSIS).
• Support data extraction, transformation, cleansing, and loading activities for Dynamics CRM/365 implementations.
• Develop and maintain SQL queries, stored procedures, and SSIS packages.
• Participate in data mapping, reconciliation, and validation activities.
• Assist with migration of structured and unstructured data, including attachments and correspondence records.
• Support testing, defect resolution, and production deployment activities.
• Collaborate with senior developers, business analysts, and project teams.
• Help troubleshoot data migration and integration issues.
• Prepare technical documentation and migration support materials.

Junior CRM Data Migration Developer

• Develop and maintain enterprise data and analytics strategy documents, roadmaps, and improvement plans (including POA&Ms) for DLA J6TF
• Design and support an enterprise data governance framework — governance structure documentation, data ownership assignments, data stewardship roles, and operating procedures
• Manage metadata per DoD standards, including DoD Metadata Registry (DoD 8320 series) and Net-centric Enterprise Services requirements
• Provide architecture recommendations for DLA analytical platforms — data ingestion, storage, processing, and delivery layers
• Support enhancement of DLA's Enterprise Data Warehouse (EDW) — logical and physical schema design, data source integration, and analytical layer development
• Develop data dictionaries, data inventories, and data lineage documentation across DLA's multi-system enterprise environment
• Identify and register authoritative data sources across DLA data domains; document authoritative source designations per governance policy
• Conduct data quality assessments — profile source data, document quality issues, and produce remediation recommendations
• Support data governance working groups and data stewardship council activities — facilitation, briefings, and deliverable preparation
• Develop and maintain artifacts including logical data models, conceptual data models, metadata management plans, and enterprise architecture alignment documents
• Prepare monthly progress reports, IPR briefing charts, and deliverables for COR review

Senior Data Architect

• Lead the design, develop and implement data models for enterprise-level applications and systems
• Create short-term tactical solutions to achieve long-term objectives and an overall data management roadmap
• Propose and collaborate on plans for security, backup, disaster recovery, business continuity, and archiving
• Oversee the development team by monitoring and reviewing the quality, performance, security, and scalability of solutions

Enterprise Data Architect

• Lead and mentor a team of data engineers, conducting code reviews and ensuring development standards
• Support troubleshooting and incident management for data-related issues in production
• Collaborate with business stakeholders, data scientists, and other team members to gather requirements and translate them into technical specifications
• Lead the design, development and deployment of scalable and high-performance data pipelines using Azure Databricks
• Optimize pipeline performance, cost, and scalability in the Azure cloud environment
• Implement data quality checks and validation procedures to ensure the accuracy and integrity of data
• Collaborate with data scientists and analysts to operationalize and deploy machine learning models
• Define the end-to-end Lakehouse architecture using Delta Lake, implementing medallion architecture (Bronze, Silver, Gold layers)
• Oversee the development of robust, scalable batch and streaming ETL/ELT pipelines using PySpark, Scala, and SQL
• Integrate real-time and batch data sources using Apache Kafka and ADF
• Implement Unity Catalog for unified governance, data security, fine-grained access control (RBAC), privacy measures, and data lineage tracking
• Tune Spark jobs and Databricks clusters to maximize throughput while maintaining cost efficiency

Data Platform Manager

• Design, build, and maintain scalable ERP data pipelines for platforms such as Workday and enterprise systems
• Own end‑to‑end lifecycle of ERP data pipelines (ingestion → transformation → delivery)
• Support data conversion and migration initiatives (e.g., M&A, system integrations)
• Ensure pipeline reliability, recoverability, and stable production operations
• Troubleshoot pipeline failures and resolve root causes of data defects and operational issues
• Partner with business analysts and stakeholders to translate requirements into data solutions
• Implement and enforce SDLC best practices (version control, code reviews, CI/CD, release management)
• Develop and maintain data quality controls (freshness, completeness, anomaly detection, schema management)
• Detect and manage schema drift and downstream data issues proactively
• Build and maintain monitoring, alerting, and observability for data pipelines
• Optimize performance, scalability, and cost of data workloads
• Track operational KPIs (e.g., pipeline success rates, SLAs, incident trends, recovery times)
• Create runbooks, documentation, and audit‑ready operational processes
• Collaborate with data governance, security, and compliance teams on controls and data access
• Support analytics, reporting, and integration teams with reliable, well‑governed datasets
• Communicate effectively with technical and non‑technical stakeholders and escalate risks early

Senior ERP Data Engineer

• Support QVC and collaborate with an Agile team to build data pipelines and models
• Drive data-driven decision-making and personalization
• Shape both back-end data structures and how insights are delivered
• Design data architecture, models, APIs, and pipelines aligned with data architecture principles and team goals
• Solve complex data challenges to deliver insights that drive outcomes
• Create data products for analytics and data science teams to improve their productivity and insights
• Lead the evaluation, implementation, and deployment of new tools for analytics data engineering
• Develop and deliver plans on analytic data engineering capabilities and standards
• Partner with product teams and solution architects to develop scalable, reliable technical architectures
• Mentor peers through code reviews, collaboration, and technical coaching
• Explore and learn about machine learning, data science, AI, and personalization technologies to evolve our approach

• Design, build, and maintain saleable data pipelines within Snowflake from multiple sources
• Build and maintain medallion architecture (bronze/silver/gold layers) for data lake solutions
• Design data models that support both businesses intelligence and AI requirements
• Lead cloud-native data platform evolution, integrating Snowflake with AWS.
• Collaborate with business analysts, data scientists, and stakeholders to understand data requirements and translate them into technical specifications.
• Implement data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of data assets.
• Develop and maintain comprehensive documentation for data pipelines, models, and processes.
• Optimize query performance and resource utilization to reduce costs and improve processing times.
• Participate in on-call rotation to support production data systems and resolve incidents.

• Designs, develops and optimizes highly complex, specialized data warehouses
• Analyzes business intelligence data and makes recommendations for growth and integration
• Works with IT and business customers to develop requirements for data warehouse architecture
• Provides support and guidance to ensure architecture aligns with BI application needs
• Leads and consults on implementation of data warehouses and ensures data integrity

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