Wild Ducks · 2 days ago
Data Platform Engineer
Austin, TX
Full-time
Hybrid
Mid, Senior LevelWild Ducks is building a real-time, event-driven forecasting platform, and they are seeking a Data Platform Engineer to own their data architecture end-to-end. This role involves responsibilities such as database architecture, performance optimization, data engineering, and ensuring data integrity across various domains.
Computer Software
Responsibilities
Own the structure, evolution, and quality of our Postgres schemas across all domains (forecasting, demand ingestion, eventing, TSL, geolocation, etc.)
Design high-integrity, multi-tenant-safe data models using UUID PKs, real foreign keys, RLS, partitioning, and row-level auditability
Work with engineering leadership to shape the canonical data model behind demand, installed base, forecasting, TSL, and analytics workloads
Ensure schemas align with business invariants and support future growth without fragmentation
Design and maintain table partitioning strategies (time-based and tenant-based) for high-throughput workloads
Diagnose and resolve performance bottlenecks: query tuning, index planning, materialized views, caching layers
Build and maintain observability around database health, query performance, storage growth, and index efficiency
Partner with ingestion, forecasting, and orchestration teams to ensure all pipelines read/write safely with proper RLS, locking discipline, and idempotency
Improve and harden cross-service data flows (Django ORM, SQLAlchemy, Temporal activities, Pulsar event persistence)
Maintain the event → DB → projection/read-model lifecycle and ensure correctness across domains
Define and enforce our schema migration standards (zero-downtime migrations, NOT VALID → VALID foreign keys, safe column evolution)
Build internal tools that keep the data platform clean (schema diffing, RLS linters, migration validation pipelines)
Partner with the CTO to establish data governance, retention, backup, and archiving policies
Work closely with product and engineering teams to design data models that match domain needs (parts, stock_location, routing, forecasting, TSL)
Support analytics and insights teams by defining readable, durable read models
Participate in architectural reviews and major feature design to ensure the data layer is scalable and future-proof
Qualification
Required
Own the structure, evolution, and quality of our Postgres schemas across all domains (forecasting, demand ingestion, eventing, TSL, geolocation, etc.)
Design high-integrity, multi-tenant-safe data models using UUID PKs, real foreign keys, RLS, partitioning, and row-level auditability
Work with engineering leadership to shape the canonical data model behind demand, installed base, forecasting, TSL, and analytics workloads
Ensure schemas align with business invariants and support future growth without fragmentation
Design and maintain table partitioning strategies (time-based and tenant-based) for high-throughput workloads
Diagnose and resolve performance bottlenecks: query tuning, index planning, materialized views, caching layers
Build and maintain observability around database health, query performance, storage growth, and index efficiency
Partner with ingestion, forecasting, and orchestration teams to ensure all pipelines read/write safely with proper RLS, locking discipline, and idempotency
Improve and harden cross-service data flows (Django ORM, SQLAlchemy, Temporal activities, Pulsar event persistence)
Maintain the event → DB → projection/read-model lifecycle and ensure correctness across domains
Define and enforce our schema migration standards (zero-downtime migrations, NOT VALID → VALID foreign keys, safe column evolution)
Build internal tools that keep the data platform clean (schema diffing, RLS linters, migration validation pipelines)
Partner with the CTO to establish data governance, retention, backup, and archiving policies
Work closely with product and engineering teams to design data models that match domain needs (parts, stock_location, routing, forecasting, TSL)
Support analytics and insights teams by defining readable, durable read models
Participate in architectural reviews and major feature design to ensure the data layer is scalable and future-proof
You think in systems, invariants, and lifecycle, not just tables
You are obsessive about correctness, consistency, and clarity in data structures
You communicate clearly and collaboratively with engineers across backend, eventing, forecasting, and DevOps
You enjoy both designing new models and untangling old ones
Deep familiarity with Postgres: querying, indexing, performance tuning, partitioning, RLS, WAL, explain plans
Experience designing multi-tenant schemas with strong security boundaries
Knowledge of event-driven systems and how data behaves around Pulsar/Kafka, outbox patterns, and projection models
Comfort with Python (Django, SQLAlchemy) or equivalent backend frameworks
Experience building or evolving data architectures for SaaS, logistics, or forecasting platforms
Experience supporting high-throughput ingestion systems
Experience designing data warehouse adjacencies (future ClickHouse, OLAP layers) is a plus
Preferred
Bonus: familiarity with forecasting concepts (demand, installed base, TSL, planning nodes) or willingness to learn
Company
Wild Ducks
At Wild Ducks, we help complex, multi-location organizations modernize their supply chains through precision planning, real-time visibility, and intelligent automation.
11-50 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase