AWS Data Engineer Manager

Location: Dallas, TX (additional locations: Atlanta - Peachtree Rd, Austin, Charlotte, Chicago, Denver, Houston, Miami, Orlando, Philadelphia, Phoenix, Tampa)

About the role

Protiviti’s mission: We Care. We Collaborate. We Deliver. At every level, we champion leaders who live our values of integrity, inclusion, innovation, and commitment to success. Integrity guides our way, inclusion moves us forward together, innovation creates new destinations, and our commitment to success empowers us to deliver on our vision to be the most trusted global consulting firm.

Responsibilities

• Lead the design and delivery of scalable, secure, and reliable data platforms and pipelines on AWS for clients across multiple industries.
• Partner with clients to translate complex business and data requirements into practical AWS data solutions.
• Lead significant data engineering workstreams, guide delivery teams, and remain actively involved in architecture, design, and technical decision‑making.
• Develop trusted client relationships.
• Mentor Consultants and Senior Consultants.
• Contribute to proposals.
• Help expand Protiviti’s AWS data engineering capabilities.
• Successfully lead architecture and delivery outcomes for multiple significant AWS data engineering engagements.
• Become a trusted advisor to client data, analytics, and technology leaders.
• Consistently convert ambiguous requirements into implementable data architectures, backlogs, and delivery plans.
• Improve the quality and consistency of data engineering through reusable standards, patterns, and accelerators.
• Develop team capability through coaching, technical reviews, and knowledge sharing.
• Help create well‑scoped follow‑on work that addresses client data needs and produces measurable outcomes.

Skills

• Data ingestion from diverse formats (parquet, Excel, CSV, EDI, email attachments, PDF)
• ETL pipeline design (AWS Glue, Lambda, or Step Functions)
• SQL and data modeling (building the agent knowledge base from JDE extracts, contract records, spend data)
• Data quality profiling and validation (Phase 0 scorecard deliverable)
• Familiarity with procurement data structures (PO lines, item master, vendor master, contract terms) – can learn on the job but needs to be comfortable with messy enterprise data
• S3, Athena, or DuckDB for ad‑hoc analysis
• Ability to produce clean export