Responsibilities
- Create high-performance, reusable software components focused on scalability, consistency, and long-term maintainability.
- Work closely with product and FinOps teams to turn planning and budgeting needs into robust technical solutions, guiding features from concept to production.
- Develop flexible interfaces for forecast data consumption, including Lightdash models, alert formats, and APIs, tailored for finance and capacity use cases.
- Help design and implement new features in the Forecast Engine while improving existing simulation, validation, and publishing workflows.
- Integrate automated testing into development pipelines to ensure output consistency, including byte-level reproducibility and forecast accuracy regression checks.
- Engage in code and design reviews to uphold standards in performance, testability, and long-term maintainability.
- Build test strategies that cover functional correctness, integration points, regression coverage, and forecast accuracy using real-world actuals.
- Encourage continuous learning by sharing engineering best practices and quality standards across the team.
- Champion engineering excellence, knowledge exchange, and disciplined quality practices within the engineering group.
- Lead the architecture of the Forecast Engine and its automation layer, including scheduled runs, variance tracking, and alerting systems.
- Make key technical decisions around forecast frequency, reconciliation with actuals, alert routing, and contracts between simulation and consuming services.
- Define and enforce standards for forecast automation, including idempotent execution, deterministic outputs, and strict failure handling.
- Specify how forecast signals such as budget breaches, capacity headroom, and migration drift are calculated, evaluated, and exposed.
- Advance forecasting automation through innovation, including responsible integration of AI/ML tools to improve development speed and insight quality.
- Implement scheduled execution of the Forecast Engine using Argo Workflows, with retry logic, failure alerts, and reproducible runs.
- Build systems to track forecast variance against plans and actuals, computing differences at granular levels like provider, region, pod, and workload.
- Design alerting mechanisms for budget overruns, forecast deviations, capacity thresholds, and pipeline health, delivering notifications to Splunk and team channels.
- Connect with planning systems to ingest budget targets and export forecast results for use in planning interfaces.
- Lead the Future Capacity Reservation (FCR) integration by converting forecasted growth and migration timelines into actionable reservation recommendations, aligned with procurement timelines and avoiding duplicate reservations.
- Enhance the Rust-based simulation engine, including core logic for time periods, growth modeling, migration, routing, and capacity allocation, along with Trino input and Iceberg output pipelines.
- Maintain and expand Lightdash data marts using dbt models on forecast and variance data for consumption by finance and capacity teams.
- Define the forecast input data contract to ensure data quality issues surface immediately and are resolved at the source.
- Implement fully traceable forecast executions with recorded inputs, seeds, configurations, output locations, and performance metrics.
- Develop monitoring and observability tools for the Forecast Engine, tracking success rates, latency, memory usage, accuracy trends, and alert delivery status.
- Establish a scalable automation foundation capable of growing from a few scheduled forecasts to a comprehensive, multi-scenario forecasting program.
Work Arrangement
Remote (Worldwide)
