Senior Data Engineer

Plantbid is a rapidly growing, revenue-positive SaaS company transforming how landscaping businesses source materials. They are seeking a Senior Data Engineer to own search and data foundations, designing how data is collected, modeled, tested, and served while partnering closely with backend engineers, product, and analytics teams.

Responsibilities:

• Architect and maintain production-grade data and search systems in Python
• Build scalable, well-tested pipelines with clear SLAs and automated recovery/backfill paths
• Design and operate end-to-end propagation for denormalized data: event models, idempotent consumers, ordering, retries/DLQ, and repair jobs
• Set and monitor SLOs for data freshness and propagation latency; instrument dashboards and alerts
• Establish data quality guardrails
• Partner with product/engineering to translate business questions into reliable datasets, metrics, and search experiences
• Drive continuous improvement—CI/CD for data, performance tuning, cost controls, documentation, and knowledge sharing
• Mentor engineers on Pythonic data engineering best practices and thoughtful, readable code
• Assist in constructing RESTful API facades for your data implementations

Requirements:

• 8+ years of professional experience in data engineering or backend data systems, with deep Python expertise
• Proven Elasticsearch experience running at scale (indexing strategies, analyzers, query design, relevance tuning, monitoring, and reindex/upgrade playbooks)
• Experience running denormalized data at scale in NoSQL and search systems, with hands-on use of event-driven propagation patterns
• Demonstrated use of idempotency/versioning to ensure correctness with at-least-once delivery
• Strong SQL and performance tuning across large datasets; hands-on dbt in production (models/tests/docs/macros, incremental patterns)
• Built and operated pipelines with an orchestrator (Airflow/Prefect/Dagster or similar)
• Data modeling mastery (dimensional modeling, SCDs) and designing stable data contracts
• Comfortable defining and meeting freshness SLOs and building backfill/repair tooling
• Excellent communication; comfortable leading cross-functional initiatives and making pragmatic trade-offs
• Vector/semantic search (embeddings, hybrid search, learning-to-rank)
• Streaming/event systems (Kafka/Kinesis/Fivetran/Pub/Sub), CDC tooling, or Debezium-style patterns
• PySpark or distributed processing; pandas/Polars for transformation workflows
• Data observability platforms, lineage (OpenLineage), or expectations frameworks
• Cloud experience (AWS/GCP/Azure) and IaC (Terraform) for data infra
• Comfort using AI coding assistants (e.g., ChatGPT/Cursor) to accelerate high-quality work