Plantbid
We’re a rapidly growing, revenue-positive SaaS company transforming how landscaping businesses source materials. Our platform connects contractors with a wide network of high-quality landscape supplies, streamlining procurement and empowering their operations. We’re now hiring a Senior Data Engineer to own search and data foundations as we scale.
As our Senior Data Engineer, you’ll be the technical owner for search and data across the company. You’ll design how data is collected, modeled, tested, and served—ensuring it’s fast, trustworthy, and cost-efficient. You’ll partner closely with backend engineers, product, and analytics to deliver features and insights that move the business.
What You’ll Own
Elasticsearch: Index and schema design, analyzers/tokenizers, relevance tuning (boosting, scoring), synonym/stopword management, query DSL, vector/semantic search evaluation, reindexing/upgrade strategies, scaling & observability. Denormalized Data Propagation (NoSQL + Search): Own how changes to canonical records propagate to all denormalized copies and search indexes. Define the source of truth, publish reliable change events, and implement idempotent, ordered consumers that fan out updates across NoSQL stores and Elasticsearch. Build repair/backfill jobs to catch drift, and establish freshness objectives with dashboards and alerts. Maintain a simple lineage map and schema/contracts so field changes are safe, versioned, and backward-compatible. Pipelines, Warehouse & dbt: Design, build, and operate reliable ELT/ETL; orchestrate jobs; implement robust dbt models/tests/docs; manage incremental models, partitioning, costs, and performance in a modern cloud warehouse (e.g., Snowflake/BigQuery/Redshift). Data Modeling: Creating clear, reusable semantic layers and metrics. 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. 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. Required Skills & Qualifications
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. What We Offer
Generous profit sharing and bonus structure Equity Remote-first, generative culture Opportunity to join a fast-growing, revenue-positive company Collaborative team of experienced, supportive peers Exposure to modern data and search technologies—and the autonomy to shape them If you’re a seasoned Data Engineer who loves owning search and data end-to-end—and you write clean, Pythonic, well-tested code—we’d love to meet you. Apply now and help us build the data and search backbone powering the future of the landscape supply industry.
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Elasticsearch: Index and schema design, analyzers/tokenizers, relevance tuning (boosting, scoring), synonym/stopword management, query DSL, vector/semantic search evaluation, reindexing/upgrade strategies, scaling & observability. Denormalized Data Propagation (NoSQL + Search): Own how changes to canonical records propagate to all denormalized copies and search indexes. Define the source of truth, publish reliable change events, and implement idempotent, ordered consumers that fan out updates across NoSQL stores and Elasticsearch. Build repair/backfill jobs to catch drift, and establish freshness objectives with dashboards and alerts. Maintain a simple lineage map and schema/contracts so field changes are safe, versioned, and backward-compatible. Pipelines, Warehouse & dbt: Design, build, and operate reliable ELT/ETL; orchestrate jobs; implement robust dbt models/tests/docs; manage incremental models, partitioning, costs, and performance in a modern cloud warehouse (e.g., Snowflake/BigQuery/Redshift). Data Modeling: Creating clear, reusable semantic layers and metrics. 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. 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. Required Skills & Qualifications
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. What We Offer
Generous profit sharing and bonus structure Equity Remote-first, generative culture Opportunity to join a fast-growing, revenue-positive company Collaborative team of experienced, supportive peers Exposure to modern data and search technologies—and the autonomy to shape them If you’re a seasoned Data Engineer who loves owning search and data end-to-end—and you write clean, Pythonic, well-tested code—we’d love to meet you. Apply now and help us build the data and search backbone powering the future of the landscape supply industry.
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