Staff Data Engineer, Workiva

• 5+ years in data engineering or backend engineering with hands-on ownership of production Postgres (or comparable relational OLTP) systems
• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field, or equivalent professional experience
• Reliable internet access for any period of time working remotely, not in a Workiva office

Desirable

• Join Workiva's GRC (Governance, Risk & Compliance) Enablement team to build the transactional data foundations that power our product teams
• As a Staff Data Engineer, you'll own Postgres schema design, transactional data models, and the extraction layer that feeds our OLAP systems
• You'll work alongside an OLAP specialist on this team (same manager) to jointly own GRC data outcomes end to end, transactional models through BI and history marts, and you'll partner closely with the platform data team on shared infrastructure and tooling
• Your job is to make sure GRC product teams have data systems that are reliable, well-modeled, and easy to build against, and to raise the data fluency of engineers across the org
• This is a heavily hands-on role
• You'll design schemas, define extraction and CDC patterns, and mentor engineers who need to write better queries and reason about relational data at scale
• Together with the OLAP specialist on this team, you'll own the full data lifecycle for GRC: transactional models, BI marts, and history marts
• Transactional database design & operations
• Design, review, and evolve Postgres schemas for GRC product services, balancing normalization, query performance, and application ergonomics
• Set standards for indexing, constraints, partitioning, and connection pooling; identify and resolve performance bottlenecks in production
• Own migration strategy and tooling (schema changes, backfills, zero-downtime deploys) across services
• OLTP-to-OLAP extraction (owned) / transformation (shared boundary with OLAP)
• Own change-data-capture and extraction: what leaves Postgres, in what shape, on what cadence, and how upstream schema changes are communicated before they break downstream models
• Define the data contract at the landing zone with the OLAP specialist so extraction output is stable and well-documented; you own upstream reliability, they own what happens after landing
• Where dbt/ETL logic sits closer to source-of-truth semantics (e.g., early-stage transaction or history-mart models), collaborate with the OLAP specialist to determine ownership on a per-project basis, documented explicitly rather than left implicit
• Data modeling & query performance
• Establish relational modeling patterns and review standards so engineers build consistent, maintainable schemas
• Optimize SQL for transactional workloads (query plans, indexing strategy, lock contention, multi-join performance at scale)
• Define data contracts between services so schema changes don't silently break consumers, including downstream OLAP consumers
• Reliability, security & compliance
• Implement data integrity checks, replication/failover practices, and monitoring for transactional data health and extraction pipelines
• Work within regulated release pipelines using GitOps/CI/CD; manage schema and pipeline promotions through dev/test/prod with gated approvals
• Apply RBAC, audit logging, and encryption standards aligned with Workiva's information-protection policies, at the OLTP layer and extraction layer specifically
• Technical leadership & enablement
• Establish schema design patterns, SQL coding standards, and documentation so engineers can confidently build against Postgres without escalating every decision
• Mentor engineers on relational modeling, query optimization, and transactional system fundamentals; lead incident resolution and root-cause analysis for production data issues, including extraction failures
• Partner with the OLAP specialist to define and document where responsibility splits, and revisit that boundary as the platform matures
• Partner with the platform data team on infrastructure decisions (warehouse provisioning, streaming infra, CI/CD tooling) while retaining ownership of GRC-specific schema design, modeling standards, and data quality outcomes; this team owns the "what and how" of GRC data models, platform data team owns the "on what infrastructure."