Senior Data Engineer

RS21•Albuquerque, NM

12d•$0 - $140,000

About The Position

The Senior Data Engineer is a technically deep, independently operating practitioner who designs, builds, and maintains the data foundations that power RS21's analytics, AI, and LLM-driven applications across both product and professional services work. This role focuses on data readiness, cloud-native data architecture, and AI enablement, ensuring that LLM-powered systems are supported by reliable, scalable, and high-quality data pipelines. At the Senior level, this person does not require day-to-day direction. They lead pipeline architecture decisions, establish data contracts for their workstreams, mentor junior and core-level engineers, and serve as a reliable technical contributor in client-facing engagements. They translate business and product requirements into well-structured, maintainable technical solutions, and they understand how their work connects to the broader delivery plan. This role bridges hands-on execution with team-level technical leadership. The Senior Data Engineer is the person a project team depends on to own the data layer end to end, flag problems before they become blockers, and bring enough communication skill to engage directly with clients and cross-functional partners without requiring a translator.

Requirements

Technically deep, independently operating practitioner
Designs, builds, and maintains data foundations
Focuses on data readiness, cloud-native data architecture, and AI enablement
Ensures LLM-powered systems are supported by reliable, scalable, and high-quality data pipelines
Leads pipeline architecture decisions
Establishes data contracts for their workstreams
Mentors junior and core-level engineers
Serves as a reliable technical contributor in client-facing engagements
Translates business and product requirements into well-structured, maintainable technical solutions
Understands how their work connects to the broader delivery plan
Owns the data layer end to end
Flags problems before they become blockers
Engages directly with clients and cross-functional partners
Experience with batch and real-time ingestion, transformation, and delivery
Experience designing, building, and maintaining scalable ETL/ELT workflows
Experience with data quality, lineage, monitoring, and observability
Experience establishing data contracts and pipeline standards
Experience supporting batch and streaming ingestion patterns
Experience designing and implementing real-time data pipelines
Experience designing and implementing data pipelines for LLM and AI use cases
Experience with document and unstructured data ingestion
Experience with data preprocessing, enrichment, and embedding generation
Experience with vector store integration and retrieval-optimized data structures
Experience leading embedding pipeline architecture and vector store configuration
Experience ensuring data freshness, lineage, and governance for AI-powered systems
Experience optimizing data structures and retrieval patterns
Experience contributing to RAG pipeline design
Experience architecting and provisioning AWS services (S3, Glue, Glue Catalog, Redshift, Athena, EMR, Kinesis, MSK, Lambda, Step Functions, EventBridge)
Experience with FedRAMP-compliant architecture design
Experience applying security and access control patterns using Lake Formation and IAM
Experience contributing to reusable reference architectures
Experience partnering with platform and DevOps teams
Experience applying infrastructure-as-code and automation practices
Experience participating in client discovery and requirements-gathering sessions
Experience translating operational needs into concrete data architecture recommendations
Experience communicating clearly with both technical and non-technical stakeholders
Experience assessing and documenting client data readiness
Experience identifying gaps and proposing remediation paths
Experience supporting the technical narrative during delivery
Experience contributing to solution design documents, architecture diagrams, and client-facing documentation
Experience building trust with client counterparts
Experience owning the data engineering workstream within a project delivery plan
Experience with task decomposition, estimation, and sequencing in Jira
Experience understanding how individual tickets connect to the larger delivery arc
Experience surfacing dependencies or risks
Experience facilitating technical planning and review ceremonies
Experience providing clear updates to project managers and technical leads
Experience partnering with project managers and product owners
Experience coordinating across engineering, platform, analytics, and ML teams
Experience supporting the evolving data architecture behind RS21 product capabilities
Experience assessing and improving internal and client data readiness
Experience translating business, product, and client needs into scalable data architectures
Experience documenting data architectures, pipeline designs, and integration patterns
Experience mentoring junior and core-level data engineers
Experience with code reviews, architecture critiques, and hands-on guidance
Experience identifying skill gaps and working with technical leadership to address them
Experience contributing to internal playbooks, onboarding materials, and engineering standards
Experience helping team members understand the business context behind their work
Experience collaborating closely with developers building LLM features
Experience working with product, analytics, and technical leadership
Experience communicating findings, risks, and architectural decisions clearly
Experience contributing to RS21 proposal efforts and technical volume sections

Responsibilities

Lead pipeline architecture decisions for assigned workstreams, including batch and real-time ingestion, transformation, and delivery to analytics and AI layers.
Design, build, and maintain scalable ETL/ELT workflows with strong data quality, lineage, monitoring, and observability built in from the start.
Establish data contracts and pipeline standards for the projects you own, and ensure those standards are followed by other contributors on the workstream.
Ensure data reliability, performance, and scalability across platforms; proactively surface bottlenecks and recommend improvements before they affect delivery.
Support both batch and streaming ingestion patterns, including real-time data pipeline design and implementation.
Design and implement data pipelines that support LLM and AI use cases, including document and unstructured data ingestion, data preprocessing, enrichment, and embedding generation, and vector store integration and retrieval-optimized data structures.
Lead embedding pipeline architecture and vector store configuration in collaboration with developers building LLM features.
Ensure data freshness, lineage, and governance for AI-powered systems.
Optimize data structures and retrieval patterns to support efficient LLM context usage.
Contribute to RAG pipeline design and maintain awareness of foundation model data requirements across active engagements.
Architect and provision AWS services to support data and AI workloads, including S3, Glue, Glue Catalog, Redshift, Athena, EMR, Kinesis, MSK, Lambda, Step Functions, and EventBridge.
Lead FedRAMP-compliant architecture design for data environments where required; apply security and access control patterns using Lake Formation and IAM.
Contribute to reusable reference architectures for data lakes, warehouses, streaming systems, and AI-ready platforms.
Partner with platform and DevOps teams to ensure secure, cost-effective, and scalable cloud deployments.
Apply infrastructure-as-code and automation practices to data platform provisioning and maintenance.
Participate in client discovery and requirements-gathering sessions, translating operational needs into concrete data architecture recommendations.
Communicate clearly with both technical and non-technical stakeholders, adapting depth and language to the audience without losing precision.
Assess and document client data readiness for analytics and AI adoption; identify gaps and propose remediation paths to the lead architect or engagement manager.
Support the technical narrative during delivery, contributing to solution design documents, architecture diagrams, and client-facing documentation.
Build working trust with client counterparts through consistency, follow-through, and clear expectation-setting.
Own the data engineering workstream within a project delivery plan, including task decomposition, estimation, and sequencing in Jira.
Understand how individual tickets connect to the larger delivery arc, and surface dependencies or risks before they block progress.
Facilitate technical planning and review ceremonies for the data workstream; provide clear updates to project managers and technical leads.
Partner with project managers and product owners to keep the data engineering track aligned with contractual and delivery constraints.
Coordinate across engineering, platform, analytics, and ML teams to ensure data pipelines meet downstream requirements.
Support the evolving data architecture behind RS21 product capabilities, including predictive and real-time ML systems.
Assess and improve internal and client data readiness for analytics and AI adoption.
Translate business, product, and client needs into scalable data architectures that can be maintained and extended by the broader team.
Document data architectures, pipeline designs, and integration patterns to support transparency and reuse across engagements.
Mentor junior and core-level data engineers through code reviews, architecture critiques, and hands-on guidance on active projects.
Identify skill gaps in team members and work with technical leadership to address them through structured coaching or pairing.
Contribute to internal playbooks, onboarding materials, and engineering standards that reduce tribal knowledge and improve team consistency.
Help team members understand the business context behind their work, connecting individual tasks to client outcomes and RS21's technical strategy.
Collaborate closely with developers building LLM features to ensure data pipelines meet AI requirements.
Work with product, analytics, and technical leadership to align data strategy with organizational and project goals.
Communicate findings, risks, and architectural decisions clearly in both written and verbal form, including client-facing documentation.
Contribute to RS21 proposal efforts and technical volume sections as a subject matter contributor.