Data Engineer

Senior Data Platform Engineer Location: Remote (LATAM, with daily overlap of US business hours) Team: Engineering / Data Platform Client: AI-powered property-tax assessment platform for US county governments Summary You'll own the data pipelines that turn millions of messy, fragmented external records into clean, reliable, audit-ready data that an AI-powered platform can trust. You'll work directly with the client and a small senior team to ingest new counties, harden multi-tenant data isolation, and make pipeline jobs autonomous and reproducible at scale. This is a high-accuracy domain where a wrong number has legal and revenue consequences, so correctness and observability matter as much as throughput. Because this is a small senior engineering team, this is not an isolated ETL-only role. The ideal candidate should be comfortable tracing data issues across pipelines, PostgreSQL, backend APIs, frontend workflows, product behavior, and user-facing validation. Requirements (Must-Haves) Excellent English communication skills — you'll interact directly with the client and US-based stakeholders, defend technical decisions, and explain data issues to non-engineers.7+ years building and operating production data pipelines, with real experience processing large-scale datasets (millions of records) — not toy projects or tutorials.Expert Python for data processing, ingestion, and transformation.Deep SQL and PostgreSQL — complex queries, indexing, query optimization, and debugging performance under real production load.Hands-on AWS experience for production data workflows, including Glue Jobs, Lambda, Step Functions, EventBridge, RDS/PostgreSQL, object storage, monitoring, retries, failure handling, and cost-aware execution. Experience with adjacent AWS services is expected as the architecture evolves.Proven ability to design data workflows for quality, correctness, and auditability — including validation, deduplication, anomaly detection, reconciliation against trusted sources, and clear failure visibility before bad data reaches users or downstream systems.Comfort owning data workflows end-to-end — ingestion, transformation, orchestration, scheduling, observability, monitoring, alerting, failure recovery, and continuous reliability improvement with minimal hand-holding.Backend/API development experience, preferably with Node.js/TypeScript, able to contribute to backend services around data workflows, expose pipeline outputs through APIs, debug integration issues, and understand how data changes affect product behavior.Working knowledge of LLM-driven data pipelines (OpenAI, Claude, Gemini, or similar, and on-prem deployed LLMs) and validating/structuring AI-generated output.Experience with multi-tenant data architectures and tenant data isolation.Workflow orchestration experience for autonomous, step-controllable jobs.Experience with observability and operational tooling for data workflows, including structured logs, metrics, alerts, traces, run history, failure visibility, and cost monitoring. Requirements (Nice-to-Haves) Data warehousing experience (Snowflake, BigQuery, Redshift, or Databricks), warehouse architectures, dbt, and analytics-engineering / data modeling.React/frontend experience (Typescript) — strongly preferred because pipeline output directly affects user-facing workflows, QA screens, validation tools, and product behavior.Infrastructure-as-code (Terraform) and Docker for reproducible local and cloud environments. Bonus Points Experience with government, civic, real-estate/proptech, or other regulated high-accuracy data where outputs must be auditable and legally defensible.Built data-quality or QA tooling that let non-engineers validate results at scale.Track record of taking over a fragile, fast-moving pipeline and making it boring, reliable, and observable.Open-source contributions, writing, or speaking on data engineering.

Senior Data Platform Engineer

Senior Data Platform Engineer

Location: Remote (LATAM, with daily overlap of US business hours)

Location:

Team: Engineering / Data Platform

Team:

Client: AI-powered property-tax assessment platform for US county governments

Client:

Summary

Summary

You'll own the data pipelines that turn millions of messy, fragmented external records into clean, reliable, audit-ready data that an AI-powered platform can trust. You'll work directly with the client and a small senior team to ingest new counties, harden multi-tenant data isolation, and make pipeline jobs autonomous and reproducible at scale. This is a high-accuracy domain where a wrong number has legal and revenue consequences, so correctness and observability matter as much as throughput. Because this is a small senior engineering team, this is not an isolated ETL-only role. The ideal candidate should be comfortable tracing data issues across pipelines, PostgreSQL, backend APIs, frontend workflows, product behavior, and user-facing validation.

Requirements (Must-Haves)

Requirements (Must-Haves)

• Excellent English communication skills — you'll interact directly with the client and US-based stakeholders, defend technical decisions, and explain data issues to non-engineers.

Excellent English communication skills — you'll interact directly with the client and US-based stakeholders, defend technical decisions, and explain data issues to non-engineers.

Excellent English communication skills

• 7+ years building and operating production data pipelines, with real experience processing large-scale datasets (millions of records) — not toy projects or tutorials.

7+ years building and operating production data pipelines, with real experience processing large-scale datasets (millions of records) — not toy projects or tutorials.

7+ yearslarge-scale datasets (millions of records)

• Expert Python for data processing, ingestion, and transformation.

Expert Python for data processing, ingestion, and transformation.

Expert Python

• Deep SQL and PostgreSQL — complex queries, indexing, query optimization, and debugging performance under real production load.

Deep SQL and PostgreSQL — complex queries, indexing, query optimization, and debugging performance under real production load.

Deep SQL and PostgreSQL

• Hands-on AWS experience for production data workflows, including Glue Jobs, Lambda, Step Functions, EventBridge, RDS/PostgreSQL, object storage, monitoring, retries, failure handling, and cost-aware execution. Experience with adjacent AWS services is expected as the architecture evolves.

Hands-on AWS experience for production data workflows, including Glue Jobs, Lambda, Step Functions, EventBridge, RDS/PostgreSQL, object storage, monitoring, retries, failure handling, and cost-aware execution. Experience with adjacent AWS services is expected as the architecture evolves.

production data workflows, including Glue Jobs, Lambda, Step Functions, EventBridge, RDS/PostgreSQL, object storage, monitoring, retries, failure handling, and cost-aware execution.

• Proven ability to design data workflows for quality, correctness, and auditability — including validation, deduplication, anomaly detection, reconciliation against trusted sources, and clear failure visibility before bad data reaches users or downstream systems.

Proven ability to design data workflows for quality, correctness, and auditability — including validation, deduplication, anomaly detection, reconciliation against trusted sources, and clear failure visibility before bad data reaches users or downstream systems.

• Comfort owning data workflows end-to-end — ingestion, transformation, orchestration, scheduling, observability, monitoring, alerting, failure recovery, and continuous reliability improvement with minimal hand-holding.

Comfort owning data workflows end-to-end — ingestion, transformation, orchestration, scheduling, observability, monitoring, alerting, failure recovery, and continuous reliability improvement with minimal hand-holding.

• Backend/API development experience, preferably with Node.js/TypeScript, able to contribute to backend services around data workflows, expose pipeline outputs through APIs, debug integration issues, and understand how data changes affect product behavior.

Backend/API development experience, preferably with Node.js/TypeScript, able to contribute to backend services around data workflows, expose pipeline outputs through APIs, debug integration issues, and understand how data changes affect product behavior.

• Working knowledge of LLM-driven data pipelines (OpenAI, Claude, Gemini, or similar, and on-prem deployed LLMs) and validating/structuring AI-generated output.

Working knowledge of LLM-driven data pipelines (OpenAI, Claude, Gemini, or similar, and on-prem deployed LLMs) and validating/structuring AI-generated output.

LLM-driven data pipelines

• Experience with multi-tenant data architectures and tenant data isolation.

Experience with multi-tenant data architectures and tenant data isolation.

multi-tenant data architectures

• Workflow orchestration experience for autonomous, step-controllable jobs.

Workflow orchestration experience for autonomous, step-controllable jobs.

Workflow orchestration

• Experience with observability and operational tooling for data workflows, including structured logs, metrics, alerts, traces, run history, failure visibility, and cost monitoring.

Experience with observability and operational tooling for data workflows, including structured logs, metrics, alerts, traces, run history, failure visibility, and cost monitoring.

Requirements (Nice-to-Haves)

Requirements (Nice-to-Haves)

• Data warehousing experience (Snowflake, BigQuery, Redshift, or Databricks), warehouse architectures, dbt, and analytics-engineering / data modeling.

Data warehousing experience (Snowflake, BigQuery, Redshift, or Databricks), warehouse architectures, dbt, and analytics-engineering / data modeling.

• React/frontend experience (Typescript) — strongly preferred because pipeline output directly affects user-facing workflows, QA screens, validation tools, and product behavior.

React/frontend experience (Typescript) — strongly preferred because pipeline output directly affects user-facing workflows, QA screens, validation tools, and product behavior.

• Infrastructure-as-code (Terraform) and Docker for reproducible local and cloud environments.

Infrastructure-as-code (Terraform) and Docker for reproducible local and cloud environments.

Bonus Points

Bonus Points

• Experience with government, civic, real-estate/proptech, or other regulated high-accuracy data where outputs must be auditable and legally defensible.

Experience with government, civic, real-estate/proptech, or other regulated high-accuracy data where outputs must be auditable and legally defensible.

government, civic, real-estate/proptech, or other regulated high-accuracy data

• Built data-quality or QA tooling that let non-engineers validate results at scale.

Built data-quality or QA tooling that let non-engineers validate results at scale.

• Track record of taking over a fragile, fast-moving pipeline and making it boring, reliable, and observable.

Track record of taking over a fragile, fast-moving pipeline and making it boring, reliable, and observable.

• Open-source contributions, writing, or speaking on data engineering.

Open-source contributions, writing, or speaking on data engineering.