Note: The job is a remote job and is open to candidates in USA. RS21 is hiring a Senior Data Engineer to design, build, and maintain data foundations for analytics, AI, and LLM-driven applications across product and professional services work. The role leads data pipeline architecture and cloud-native data delivery, enables AI use cases, and provides technical leadership, client engagement, and mentorship.
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
- 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
Skills
- Bachelor's degree or equivalent experience in data engineering, computer science, or a related field
- 5+ years of hands-on data engineering experience, with demonstrated progression into senior or lead responsibilities
- Strong hands-on experience with AWS data services, including S3, Glue, Redshift, Athena, Kinesis or MSK, Lambda, and Lake Formation
- Proven ability to design and deliver production-grade ETL/ELT pipelines and data warehousing solutions
- Solid understanding of streaming architectures and real-time data pipeline design
- Experience supporting AI or LLM-adjacent data workflows, including embedding pipelines and vector store integration
- Ability to communicate clearly with both technical and non-technical stakeholders, including direct client interaction
- Experience decomposing and tracking delivery work in Jira or equivalent tooling
- Strong problem-solving skills and architectural reasoning at the workstream level
- AWS certifications: Solutions Architect Associate (SAA-C03), Data Engineer Associate (DEA-C01), or equivalent
- Databricks Certified Data Engineer Associate and/or dbt Certified Developer
- Experience with infrastructure-as-code tools such as Terraform or CDK
- Background in consulting, professional services, or multi-client delivery environments
- Familiarity with data governance frameworks, data cataloging, and lineage tooling
- Experience with Databricks, dbt, and Airflow or Prefect orchestration
- Exposure to vector databases such as Amazon OpenSearch, Pinecone, or pgvector
- Exposure to DoD, federal, or regulated-sector data environments; FedRAMP-compliant architecture experience a plus
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