Senior Data Architect

Remote

Actively hiring, first seen by us 12 days ago

Pay: USD 7,000–8,800/month — pay source
Compensation: The salary range offered for this position varies from (USD) $7,000 - $8,800 per month, depending on seniority and skillset. This position is for a full time contract, through a payroll platform.
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Work setting: Remote — work setting source
Listed location: Remote
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Employment: Contract — employment source
Employment type in the employer’s posting: Contract
From the employer’s posting

What you’ll work on

Design and own the end-to-end lakehouse architecture on Databricks, using Medallion (bronze/silver/gold) patterns.
Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python.
Lead platform migrations (e.g., legacy data warehouse or Hadoop/EMR to Databricks) with measurable gains in cost and run time.

From the employer’s posting

Responsibilities: Design and own the end-to-end lakehouse architecture on Databricks, using Medallion (bronze/silver/gold) patterns. Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python.

Design and own the end-to-end lakehouse architecture on Databricks, using Medallion (bronze/silver/gold) patterns. Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python. Lead platform migrations (e.g., legacy data warehouse or Hadoop/EMR to Databricks) with measurable gains in cost and run time.

Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python. Lead platform migrations (e.g., legacy data warehouse or Hadoop/EMR to Databricks) with measurable gains in cost and run time. Define data governance: Unity Catalog, fine-grained access control, auditing, lineage and compliance (GDPR and similar).

What you’ll bring

All qualifications

Core experience

10+ years in data engineering, including 5+ years hands-on with Databricks and Spark in production.
Strong Python and PySpark; working knowledge of Scala and advanced SQL.
Ability to explain design trade-offs clearly to engineers and non-technical stakeholders.
Experience mentoring engineers.
Experience building agentic or RAG solutions with LangChain, LangGraph, MLflow, OpenAI APIs and vector databases.

Qualification wording

10+ years in data engineering, including 5+ years hands-on with Databricks and Spark in production.

Strong Python and PySpark; working knowledge of Scala and advanced SQL.

Ability to explain design trade-offs clearly to engineers and non-technical stakeholders.

Experience mentoring engineers.

Experience building agentic or RAG solutions with LangChain, LangGraph, MLflow, OpenAI APIs and vector databases.

Apply at GoFasti

Posting record

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Actively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 23, 202612 days ago
Recorded sightings: 16
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 12

Similar jobs usually come down between day 7 and day 53.

From 7,804 similar postings that came down. Coming down is not always a hire.

GoFasti is hiring

4 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
Scala
SQL
AWS
Azure
Databricks
GCP
Hadoop
MLflow
NoSQL
Spark
Terraform
PySpark

Where the posting names them

- Design and own the end-to-end lakehouse architecture on Databricks, using Medallion (bronze/silver/gold) patterns. - Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python. - Lead platform migrations (e.g., legacy data warehouse or Hadoop/EMR to Databricks) with measurable gains in cost and run time.

- Model data for analytics and operations (OLTP/OLAP, dimensional, SQL and NoSQL). - Set engineering standards: CI/CD, unit and integration tests, code review, reusable Python packages, Databricks Asset Bundles. - Manage infrastructure as code with Terraform and drive FinOps practices to control cloud spend.

- 10+ years in data engineering, including 5+ years hands-on with Databricks and Spark in production. - Strong Python and PySpark; working knowledge of Scala and advanced SQL. - Proven record designing data lakes, data warehouses and lakehouses, including at least one large migration you led.

- Define data governance: Unity Catalog, fine-grained access control, auditing, lineage and compliance (GDPR and similar). - Model data for analytics and operations (OLTP/OLAP, dimensional, SQL and NoSQL). - Set engineering standards: CI/CD, unit and integration tests, code review, reusable Python packages, Databricks Asset Bundles.

- Proven record designing data lakes, data warehouses and lakehouses, including at least one large migration you led. - Production experience on at least one major cloud (AWS, Azure or GCP). - Hands-on data governance with Unity Catalog, access controls, lineage and auditing.

- Databricks certifications (e.g., Platform Administrator, Data Engineer Professional). - AWS Solutions Architect or equivalent cloud architecture certification. - Multi-cloud experience across AWS, Azure and GCP.

- AWS Solutions Architect or equivalent cloud architecture certification. - Multi-cloud experience across AWS, Azure and GCP. - Experience building agentic or RAG solutions with LangChain, LangGraph, MLflow, OpenAI APIs and vector databases.

- Hands-on data governance with Unity Catalog, access controls, lineage and auditing. - Solid software engineering practice: Git workflows, automated testing, CI/CD (Azure DevOps, GitHub Actions or similar). - Infrastructure as code with Terraform.

Responsibilities: - Design and own the end-to-end lakehouse architecture on Databricks, using Medallion (bronze/silver/gold) patterns. - Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python.

- Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python. - Lead platform migrations (e.g., legacy data warehouse or Hadoop/EMR to Databricks) with measurable gains in cost and run time. - Define data governance: Unity Catalog, fine-grained access control, auditing, lineage and compliance (GDPR and similar).

Requirements: - 10+ years in data engineering, including 5+ years hands-on with Databricks and Spark in production. - Strong Python and PySpark; working knowledge of Scala and advanced SQL.

It's a plus: - Databricks certifications (e.g., Platform Administrator, Data Engineer Professional). - AWS Solutions Architect or equivalent cloud architecture certification.

- Manage infrastructure as code with Terraform and drive FinOps practices to control cloud spend. - Evaluate and prototype AI use cases on the platform (LLMs, vector databases, agents, MLflow). - Advise other teams on architecture and mentor junior and mid-level engineers.

- Multi-cloud experience across AWS, Azure and GCP. - Experience building agentic or RAG solutions with LangChain, LangGraph, MLflow, OpenAI APIs and vector databases. - Real-time and IoT data processing at high volume.

- Set engineering standards: CI/CD, unit and integration tests, code review, reusable Python packages, Databricks Asset Bundles. - Manage infrastructure as code with Terraform and drive FinOps practices to control cloud spend. - Evaluate and prototype AI use cases on the platform (LLMs, vector databases, agents, MLflow).

- Solid software engineering practice: Git workflows, automated testing, CI/CD (Azure DevOps, GitHub Actions or similar). - Infrastructure as code with Terraform. - Ability to explain design trade-offs clearly to engineers and non-technical stakeholders.

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About the role

We file this job under Data Leadership, by the work the posting describes.

Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.

Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.

Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.

US government figures for computer and information systems managers

Median yearly pay, May 2025: $175,140
Expected change, 2025 to 2035: 15.8% more jobsagainst 3.5% for all jobs

The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.

Job description

View original posting ↗

We Make Remote Work Remarkable • TopTalent from LatAm

Hello! We are GoFasti, a Talent-as-a-Service. GoFasti bridges the gap between world-class developers and designers from LatAm and first-class companies around the globe.

We need an English-fluent Senior Sr. Data Architect, based in Latin America, available to work remotely.

We are looking for someone with exceptional communication and relationship-building skills, who embraces changes while maintaining strong attention to detail. An interested and proactive person, who's constantly learning and improving their skills.

Are you the one we are looking for?

Responsibilities:

Design and own the end-to-end lakehouse architecture on Databricks, using Medallion (bronze/silver/gold) patterns.
Build and optimize scalable batch and streaming pipelines in PySpark, Spark SQL and Python.
Lead platform migrations (e.g., legacy data warehouse or Hadoop/EMR to Databricks) with measurable gains in cost and run time.
Define data governance: Unity Catalog, fine-grained access control, auditing, lineage and compliance (GDPR and similar).
Model data for analytics and operations (OLTP/OLAP, dimensional, SQL and NoSQL).
Set engineering standards: CI/CD, unit and integration tests, code review, reusable Python packages, Databricks Asset Bundles.
Manage infrastructure as code with Terraform and drive FinOps practices to control cloud spend.
Evaluate and prototype AI use cases on the platform (LLMs, vector databases, agents, MLflow).
Advise other teams on architecture and mentor junior and mid-level engineers.

Requirements:

10+ years in data engineering, including 5+ years hands-on with Databricks and Spark in production.
Strong Python and PySpark; working knowledge of Scala and advanced SQL.
Proven record designing data lakes, data warehouses and lakehouses, including at least one large migration you led.
Production experience on at least one major cloud (AWS, Azure or GCP).
Hands-on data governance with Unity Catalog, access controls, lineage and auditing.
Solid software engineering practice: Git workflows, automated testing, CI/CD (Azure DevOps, GitHub Actions or similar).
Infrastructure as code with Terraform.
Ability to explain design trade-offs clearly to engineers and non-technical stakeholders.
Experience mentoring engineers.
English at C1 level or higher.

It's a plus:

Databricks certifications (e.g., Platform Administrator, Data Engineer Professional).
AWS Solutions Architect or equivalent cloud architecture certification.
Multi-cloud experience across AWS, Azure and GCP.
Experience building agentic or RAG solutions with LangChain, LangGraph, MLflow, OpenAI APIs and vector databases.
Real-time and IoT data processing at high volume.
Background in finance, telecom, payments or SaaS data domains.
Spanish or Portuguese.

Compensation:

The salary range offered for this position varies from (USD) $7,000 - $8,800 per month, depending on seniority and skillset.
This position is for a full time contract, through a payroll platform.
The talent will work remotely allocated at our client.

Here are the steps for this process:

Application review/approval > Screening interview with GoFasti's team > We build and send your profile to our client > Profile review/approval by client > Interview with the client > Hiring and onboarding.

Once you apply for the job, our team will review your resume. If it meets the requirements, we will contact you and move forward in the process.

Note for Candidates Approached Directly:
If you were contacted directly by a member of our team and are interested in this opportunity, please do not apply through this link. Instead, reach out to the person who contacted you to coordinate a meeting.

Thank you!

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