Sr. Staff Software Engineer- Eng
Location: Atlanta,GA,United States
Department: Software & Product Development
Work Location: hybrid
- Architect and evolve a cloud-native data platform using BigQuery, Dataflow, Dataform, Pub/Sub, Dataproc, Cloud Storage.
- Build scalable data ingestion, transformation, and aggregation pipelines that support high-volume HCM datasets.
- Collaborate with the Data Science team to operationalize their models, quantitative logic, and statistical frameworks—without owning statistical methodology design.
- Implement end-to-end data workflows that generate aggregated insights and derived metrics at scale.
- Engineer strong privacy, compliance, and anonymization mechanisms into all data pipelines (masking, thresholding, auditing).
- Translate analytical requirements into technical designs and execution plans in partnership with product and data stakeholders.
- Lead architecture reviews, performance tuning, cost optimization, and reliability engineering initiatives.
- Mentor engineers; guide design reviews, coding standards, and technical best practices.
- Troubleshoot complex issues involving distributed processing, data-quality anomalies, or system bottlenecks.
- Introduce new GCP tools or frameworks as needed to support long-term scalability and maintainability.
- 8+ years of software engineering experience, including 3+ years in senior or staff-level positions.
- Deep experience building distributed data systems using GCP services, including BigQuery, Dataflow (Beam), Dataform, and Pub/Sub.
- Strong programming skills in Python, Java, Scala, or similar languages used in data engineering.
- Strong understanding of data modeling, partitioning strategies, schema evolution, and cost-efficient query optimization.
- Experience designing security-first data pipelines with privacy-preserving transformations.
- Ability to break ambiguous problems into structured designs and actionable plans.
- Demonstrated ability to mentor engineers and influence technical direction across teams.
- Strong experience integrating data-science workflows or ML/analytical models into production systems.
- Experience with HCM, payroll, or other HR/enterprise data domains.
- Background supporting analytics-driven products, metrics systems, or derived-indicator pipelines.
- Experience using Dataform for SQL transformation management and production model organization.
- Familiarity with statistical concepts or time-series patterns (not for model creation, but for effective collaboration with Data Science).
- Experience with data lineage, data-quality scoring, observability systems, and automated validation frameworks.
- Experience with Cloud Composer, Dataproc, or streaming architectures for near-real-time data processing.
- Track record shaping long-term architecture or leading cross-organizational platform initiatives
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