Data Engineer

• Build, develop, and maintain scalable ELT workflows using Azure Data Factory, Fivetran, and dbt
• Monitor pipeline health and troubleshoot issues to ensure reliable data delivery
• Partner with data analysts to understand business needs and translate them into efficient, well-documented dbt data models
• Continuously refine data models to support evolving analytic requirements
• Manage and optimize the Lantern Analytic Platform
• Monitor query performance and implement data partitioning strategies
• Refine data models to enhance speed and efficiency

Senior Data Warehouse Engineer

• Review customer data from CSVs, spreadsheets, and software databases to assess structure, quality, and completeness
• Map legacy data fields to Gymdesk's platform schema using internal tools, AI (ChatGPT), or custom scripts
• Transform, clean, and normalize customer data
• Import data into Gymdesk customer environments and validate expected outputs
• Perform iterative migrations and troubleshoot errors
• Complete manual data entry when required to fill migration gaps
• Become an expert in Gymdesk's product and configure it on behalf of customers
• Coordinate with Onboarding Specialists and customers to collect requirements and data formats
• Provide status updates, clarify data issues, and explain field-level discrepancies
• Work with product and engineering to report bugs and suggest tooling enhancements
• Document migration processes and maintain internal guides
• Report directly to the VP of Customer Success and collaborate with onboarding, support, product, and engineering teams

Data Migrations Specialist

• Build, automate, and optimize infrastructure and processes enabling rapid, reliable software delivery
• Work with developers to integrate automation, improve deployment workflows, and monitor application performance
• Foster collaboration, continuous improvement, and shared ownership across engineering teams
• Identify bottlenecks, recommend architectural enhancements, and champion containerization, configuration-as-code, and automated testing
• Align tooling, processes, and infrastructure with business needs
• Design and enhance large-scale data pipelines using Hadoop, HDFS, Apache NiFi, Apache Spark, and Accumulo
• Optimize distributed systems performance and maintain resilient data ingestion and analytics patterns

• Lead development, optimization, and implementation of advanced control systems across critical infrastructure products and automation platforms
• Define and own control system architecture across PLC, BMS, EPMS, and ATS platforms
• Develop control system specifications, functional design documents, and subsystem interface definitions
• Design and develop wiring harness systems, including schematics, harness architecture, panel layouts, specifications, component selection, and transition to manufacturing
• Develop PLC/SCADA programs, HMI systems, and integrations with IT networks and facility equipment
• Define control logic, sensor integration, and communication protocols for intelligent power and cooling management
• Validate control systems through simulation, testing, and commissioning
• Lead Factory Acceptance Testing (FAT) and Site Acceptance Testing (SAT)
• Develop system-level simulations and digital twins to validate control strategies
• Architect integrations with SCADA, CMMS, and cloud-based monitoring platforms
• Establish design patterns and best practices for modular, reusable control logic
• Lead root cause analysis and resolution of complex control system issues
• Evaluate emerging technologies and automation trends
• Develop and maintain architecture, control logic, and operational documentation
• Collaborate with electrical, mechanical, software, and facilities engineering teams
• Align control system designs with business requirements, timelines, and operational constraints
• Facilitate design reviews, technical workshops, and cross-functional planning sessions
• Support commissioning and field teams during deployment, troubleshooting, and optimization
• Work with customers and supply chain partners to assess requirements, identify challenges, and design end-to-end data center infrastructure solutions

Principal Electrical Controls Engineer – Data Center Infrastructure

• Independently assess existing enterprise architecture and maintain current knowledge of its functionality
• Collaborate with multiple lines of business to deploy new architecture strategies for specific business needs
• Develop data migration and streaming strategies to evolve bank architecture and move data throughout the company
• Create an inventory of data necessary to build and implement a modern data architecture
• Maintain a repository of data architecture procedures and artifacts
• Build and manage data models for database structures and Analytics & AI/ML applications
• Establish guidelines around data architecture and modeling
• Provide training, coaching, and supervision to staff and increase staff knowledge for data
• Communicate new system strategies, designs, and methods to engineers across all lines of business
• Act with Integrity, Caring, and Excellence to achieve all-win outcomes

Senior Data Architect

• Design, build, and maintain ETL/ELT data pipelines in Python, orchestrated with Apache Airflow
• Write and maintain dbt transformation models for clean, documented reporting and analytics datasets
• Author and maintain dbt tests to validate data quality
• Build and manage ingestion and processing jobs in AWS Glue
• Query data through Amazon Athena
• Help manage and optimize the Amazon Redshift warehouse for query performance and cost efficiency
• Deploy and manage cloud resources with Terraform
• Monitor pipeline health, troubleshoot data issues, and build alerting
• Participate in code reviews
• Write documentation and contribute to data governance standards

• Build the Air Force Data Mesh Environment by combining reliable data engineering with modern application development
• Support governed discovery, access, and interoperability for distributed data products
• Design scalable ETL and ELT pipelines, data models, and orchestration workflows using Airflow, Dagster, dbt, and Python
• Develop application features and APIs with TypeScript, React, and Node.js
• Apply SQL, warehouse platforms, validation, observability, and automated tests throughout the data lifecycle
• Work with product, DevOps, infrastructure security, and mission stakeholders to turn requirements into maintainable software
• Design secure access and handling for sensitive data consistent with project security requirements
• Support Air Force government contract work

Senior Data Engineer

• Lead and manage The Helper Bees' Data Engineering & Analytics team
• Provide mentorship, guidance, and support for technical and professional development
• Design, implement, and maintain data products using Python, SQL, dbt, Airflow, BigQuery, and modern technologies
• Own and manage work intake, triage, prioritization, and Jira scheduling
• Establish a dbt-first, governed approach with tested, traceable, automated models
• Lead expansion into predictive and prescriptive analytics, including forecasting, recommendation engines, and clustering/segmentation
• Set strategic team goals aligned with company data objectives
• Collaborate with Product, Accounts, Operations, C-suite leaders, external clients, and partners
• Monitor team performance through feedback, code reviews, and training
• Produce quality, testable, maintainable code and set coding standards
• Improve deployment, sprint planning, monitoring, incident response, escalation, and workflows
• Drive adoption of AI tooling such as Cursor and Claude Code
• Debug complex issues across data pipelines and models
• Own the data team's contribution to monthly close and scorecard reporting, including client and partner reports and invoices
• Measure and improve data quality, reporting timeliness, code quality, stakeholder satisfaction, scalability, and team productivity

Manager, Data Engineering, Analytics

• Design and develop production-grade batch and real-time data pipelines for connected-vehicle telemetry, trip and session data, diagnostic signals, and vehicle-health indicators
• Build streaming applications that ingest, enrich, validate, deduplicate, curate, and publish event-driven data
• Develop reliable data products using Apache Flink, Apache Spark Structured Streaming, Java, Python, and SQL
• Work with Microsoft Azure services including Azure Kubernetes Service, Event Hubs, Azure Data Explorer, Azure Key Vault, Azure Databricks, Azure Monitor, and Application Insights
• Design and maintain data contracts, schemas, APIs, and event models using GraphQL, REST, gRPC, JSON, and cloud-event patterns
• Apply artificial intelligence and machine learning to anomaly detection, data-quality triage, predictive health signals, intelligent operations, and engineering productivity
• Build or integrate generative artificial intelligence capabilities, including large language model applications, embeddings, vector search, retrieval-augmented generation, agentic workflows, prompt engineering, evaluation, and safety guardrails
• Create automated tests, performance benchmarks, integration tests, and validation checks
• Establish observability with OpenTelemetry, Datadog, Grafana, Prometheus, dashboards, monitors, service-level objectives, and actionable alerts
• Secure data in transit and at rest and apply privacy, consent, retention, lineage, access-control, and regional compliance requirements
• Automate infrastructure and delivery using Kubernetes, Helm, Argo CD, Terraform, continuous integration, continuous delivery, and infrastructure-as-code practices
• Participate in architecture reviews, code reviews, incident response, root-cause analysis, operational readiness, and on-call support as needed
• Mentor engineers, raise technical standards, document design decisions, and contribute to continuous improvement

• Design, develop, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Databricks, and other cloud-based technologies
• Develop and optimize SQL queries, stored procedures, database processes, and data transformation solutions
• Build and support data solutions across Azure SQL, Azure SQL Managed Instance, Databricks, data warehouses, and lakehouse platforms
• Configure and support Databricks workspaces, catalogs, clusters, jobs, workflows, access controls, and deployment processes
• Support cloud infrastructure components, including storage, security, service connections, secrets, permissions, and environment configurations
• Monitor, troubleshoot, and optimize data pipelines, databases, Databricks workloads, and cloud infrastructure to improve performance and reliability
• Implement data validation, quality checks, error handling, and reconciliation processes to maintain data accuracy and integrity
• Prepare, structure, and integrate data for artificial intelligence, machine learning, automation, business intelligence, and advanced analytics use cases
• Participate in testing, code reviews, source control, CI/CD, deployment, and release management activities
• Maintain technical documentation and collaborate with engineers, architects, analysts, infrastructure teams, and business clients to deliver scalable data solutions