Senior Data Engineer

Position Summary

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.

Key Responsibilities

Data Pipelines & Platforms

• 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.

LLM Enablement & AI Data Foundations

• Design and implement data pipelines that support LLM and AI use cases, including:

• Document and unstructured data ingestion
• Data preprocessing, enrichment, and embedding generation
• 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.

Cloud Architecture (AWS)

• 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.

Client & Stakeholder Engagement

• 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.

Technical Delivery Leadership

• 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.

Product & Data Readiness Support

• 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.

Staff Development & Knowledge Sharing

• 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.

Collaboration & Communication

• 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.