• Work in a high-paced operations environment of Salesforce, the largest SaaS platform on the planet, working on the delivery and operations of scalable and innovative solutions for our modular compute infrastructure and proactively defending and securing our customers' data • Conduct all day-to-day operations for the Data Center Installation of racks/enclosures and preparation of equipment to be installed • Update cable management system as changes are made to data center cable plant • Update asset management system as equipment is added and removed from the data center • Define tasks for each project and work with all Tech and Prod teams to meet project timelines as well as provide status to team members and management on the completion of all tasks • Provide all information to assist with Data Center capacity planning for space and power • Handle all vendor resources to complete tasks as defined in SOWs • Maintain relationship with providers to ensure day-to-day operational success • Research new Data Center infrastructure equipment advancements and recommend changes as needed • Conduct all audits as required by company policies • Coordinate with the onsite delivery and shipping of all equipment • Will be part of an on-call weekly rotation shared across the Las Vegas Engineering team

• Lead and provide expert support for data collection, data validation, data visualization, and analytics initiatives • Apply disciplined methodologies for the planning, analysis, design, and development of information systems on an enterprise-wide basis or across a business sector • Develop analytical techniques and methodologies to solve complex business and technical problems • Perform strategic systems planning, business information planning, and business analysis • Organize and analyze large volumes of structured and unstructured data sets using data analytical tools • Locate, access, merge, clean, and standardize data from multiple sources, and develop derived metrics • Create and implement data collection and analysis tools using programming languages such as Python, Databricks, SQL, Scala, R, and Java • Design, script, debug, and analyze data engineering solutions • Implement and create machine learning-based tools and processes • Apply distributed and parallel processing technologies (e.g., Spark) to handle big data analytics tasks involving large data volumes • Perform SQL Server data imports from CSV and TXT files • Leverage Excel and Google Suite for data analysis, reporting, and collaboration • Utilize supporting tools and platforms such as Pentaho (data import/transformation), Azure Data Studio, GitHub, and Smartsheet as needed • Document task requirements, work completed, processes, and technical details thoroughly • Communicate effectively with stakeholders across technical and business teams • Operate independently as a subject matter expert in a fast-paced, entrepreneurial environment

Role Description Awetomaton is seeking a Staff Data Engineer to support a new customer by building and maintaining the ingestion, curation, and metadata-enrichment pipelines that power enterprise data catalog and discovery services. In this role, you will design and operate scalable data systems that transform raw and source data into reliable, well-structured datasets for catalog, search, and downstream mission use. The ideal candidate will possess a BS degree or higher and have 5+ years of experience. This position can be performed nationwide but we strongly prefer personnel local to Dayton, OH or St Louis, MO. DoD Top Secret clearance is required. For highly qualified candidates, we will sponsor. We are looking for a candidate to start in the August 2026 timeframe. Criteria for success - Communicate effectively in both written and verbal form with peers & decision makers - Demonstrate initiative when faced with limited guidance or ambiguous requirements - Strong understanding of cloud data pipelines, data warehousing solutions, and ETL/ELT orchestration tools - Proficient in defining data standards, data frameworks, and ensuring data quality Qualifications - Bachelor of Science in Data Science, Computer Science, Information Systems, or related discipline - Proficiency in Python, SQL, Spark, or similar data-centric technologies - Experience with orchestration tools (Airflow, Dagster, Argo Workflow) - Experience designing data lake and/or data warehouse structures - Strong understanding of data modeling, data quality frameworks, schema evolution, and lineage - Understanding of large-scale, cost-efficient data processing - Comfort working with containers and cloud-native deployment - Strong ability to translate complex technical concepts for non-technical audiences Desired Education and Experience - Master of Science in Data Science, Computer Science, Information Systems, or related field - Familiarity with data management/governance platforms - Cloud & platform certifications (AWS Solutions Architect, AWS Certified Data Analytics, Certified Kubernetes Application Developer, etc.) - Background in working with federal government, Department of Defense, and/or the Intelligence Community Benefits - Flexible time off totaling 36 days / year. No approvals required. - 100% 401k company match up to IRS annual limits. This is a benefit of up to $24,500 for 2026. - Health plan through Blue Cross Blue Shield with Health Savings Account (HSA). We pay 95% of employee premiums including family plans. - Employee favorite: Tech and Wellness reimbursement of $3000 / year. Treat yourself with the newest gadgets or athletic gear. No approvals required. - Annual profit sharing as 401k contribution. No set percent and dependent on annual company performance. Benefit is above and beyond 401k match. - MacBook laptop and well-equipped office spaces for optimal productivity in office and on-the-go.

Role Description We're looking for an outstanding Databricks Data Engineer to join our growing data practice — someone who builds the data and AI foundations that digital products and intelligent experiences run on. Who you are: - You are a Databricks-focused Data Engineer who understands that great data platforms are only as valuable as the products, AI workflows, and experiences they enable. - You bring deep, production-grade expertise across the Databricks platform and know how to connect platform capabilities to real business outcomes. - You thrive in ambiguity and can quickly assess a client's data landscape to recommend and implement the right solutions. - You understand that in consulting, your Databricks depth is most valuable when it connects platform capabilities to the products and experiences clients actually use. - You're as comfortable in a product design conversation as you are building a DLT pipeline. - You excel at translating complex data challenges into clear technical requirements and can confidently navigate conversations with everyone from data scientists to executives. - Your engineering principles are mature and grounded in real-world experience across various industries and scales. - You have an interest in and a curiosity about data platforms and the latest advances in data technology. What you will be doing: - Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming, with end-to-end ownership of ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles. - Architect and implement Lakehouse solutions on Databricks — medallion architecture, Delta Lake, Unity Catalog — tailored to the client's analytics, AI, and application needs. - Build and maintain Databricks transformation layers — DLT pipelines, PySpark notebooks, and dbt — with data quality constraints and SLAs baked in. - Design and maintain the data and AI foundations — Unity Catalog, Feature Store, MLflow, and Model Serving — that power production ML, agent workflows, and AI-enabled digital products. - Collaborate with product and backend engineers to design data models, APIs, and application data contracts — ensuring the platform serves the product, not just the warehouse. - Consult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions. - Adapt your approach based on project needs — sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise. - Work within multi-cloud environments — primarily AWS and Azure — anchoring data platform recommendations around Databricks where it fits the client's architecture and goals. - Champion data governance through Unity Catalog — access control, lineage, data quality policies, and compliance — as a first-class part of every engagement, not an afterthought. - Design data-to-application architectures — including Lakebase-backed services and Databricks Apps — that connect governed data to AI workflows, digital products, and user-facing experiences. - Help build Livefront's Databricks practice — contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work. Qualifications - 3-5 years of data engineering experience with at least 2 years in production Databricks environments, preferably in a consulting or client delivery context. - Solid working knowledge of AWS and Azure cloud services relevant to Databricks deployments — storage, networking, IAM, and compute — with GCP familiarity a plus. - Deep, production-grade Databricks expertise: Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, Unity Catalog (including fine-grained access control and lineage) — demonstrated through shipped production workloads, not prototypes. - Proven experience designing Lakehouse architectures — medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization — at production scale. - Hands-on experience with data pipeline testing, observability, and CI/CD for data — including unit testing, data quality frameworks, and version-controlled deployments via Git and Declarative Automation Bundles. - Strong proficiency in SQL and Python, with the ability to write clean, performant, and maintainable code. - Understanding of data modeling, schema design, and query optimization. - Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical stakeholders. - Strong problem-solving skills with the ability to navigate ambiguous requirements and deliver pragmatic solutions. - Above-average discipline and personal organization skills. - Obvious comfort with critique and peer review in the context of an iterative development process. - A demonstrated hunger for personal and professional growth. - A self-evident love and care for the craft of data engineering. Requirements - Bonus points if you have worked with real-time streaming technologies (Kafka, Kinesis, etc.). - Have hands-on experience with alternative cloud data platforms — useful context for migrations and competitive assessments, though Databricks is our primary platform focus. - Have experience in healthcare or fintech domains. - Have hands-on experience with MLOps or LLMOps on Databricks — MLflow experiment tracking, model registry, Model Serving endpoints, or Vector Search for RAG pipelines. - Have experience with Java, Go, or Scala. - Have strong illustration skills for technical diagramming and data architecture documentation. - Speak, write, and/or educate publicly about data engineering topics. - Have contributed to open-source data projects. - Hold or are actively pursuing a Databricks certification (Data Engineer Associate or Professional, or Apache Spark Developer) — we treat these as meaningful signals of platform depth, and they directly support our Databricks partner growth goals. - Have experience with Databricks Apps, or Lakebase — early familiarity with where the Databricks platform is heading is a strong differentiator. Benefits - You want to work with passionate and talented people who are always looking for ways to make things better. - You desire a work environment where respect, mutual trust, and egoless collaboration are paramount. - You want colleagues who take their work seriously but not themselves, and who know how to let loose and have a good time. - You like being part of a team with a reputation for excellence that gives back to the community by educating, mentoring, and sponsoring. - You want to work on products and accounts that have outsized impact and reach. - You believe in sweating the details, giving a damn about quality, and taking pride in going the extra mile. - You want to help build a data practice specialization from the ground up — shaping how we go to market with Databricks, what we build as accelerators, and what it means to do this kind of work at a digital product company. What to expect - When applying, please include a short note about yourself, a summary of your work experience, and a link to any public profiles you actively maintain (e.g., GitHub, LinkedIn, etc). - Our hiring process moves quickly and consists of several stages for candidates who capture our attention with their initial submission, sometimes including but not limited to a short preliminary phone interview, a series of video interviews, and a short take-home exercise, which you'll have up to a week to complete. - Compensation is $120,000 - $145,000. Additional information We go out of our way to evaluate all employees and job applicants equally based on merit, competence, and qualifications. We encourage candidates from all backgrounds to apply and consider all qualified applicants. Don't worry, every application will be reviewed by a human.