Senior Data, Platform & Solutions Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 3d ago
The date the source published, not the day we noticed it (2026-09-28). Last seen at its source 1h ago.
We have tracked this listing since 28 Sep 2026 (3 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Brazil, Argentina, Colombia, Costa Rica, Mexico, Chile
The description states no restriction of its own. This is the source's own tag.
Pay
$90k–120k/yr
Read out of the job description by us, not from a structured field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
workable employer's own board first seen 3d ago · last seen 1h ago
The listing
We are looking for a highly experienced, hands-on Senior Data Engineer who can combine deep data engineering expertise with data architecture, cloud platform ownership, and direct client engagement.
This is not a traditional back-office data engineering role where you own one pipeline or a narrow piece of a larger platform. You will work directly with clients to understand their needs, recommend the right technical approach, design the architecture, build the solution, and communicate the results.
You will typically own multiple client projects and operate as both the technical expert and a trusted advisor. The right person is comfortable running a client meeting, discussing architectural trade-offs with technical and business stakeholders, turning those decisions into working solutions, and owning delivery from beginning to end.
We are especially interested in engineers who have worked in consulting, startups, or other environments where they have had broad ownership rather than operating within one narrow function of a large data organization.
Responsibilities
- Own the technical delivery of data projects from initial client conversations through architecture, implementation, and ongoing delivery
- Lead client discussions to understand business and technical requirements, ask the right questions, and translate ambiguous problems into clear technical solutions
- Design modern data architectures spanning ingestion, transformation, compute, storage, analytics, and BI layers
- Make informed architecture recommendations based on technical trade-offs, cost, scalability, maintainability, and the client's specific requirements
- Build and maintain production data pipelines, transformations, models, and platform components
- Work hands-on with modern data platforms such as Snowflake, Databricks, BigQuery, and/or Redshift
- Build and maintain transformation workflows using SQL and modern data tooling such as dbt
- Work directly within AWS, GCP, and/or Azure environments, including platform deployment, infrastructure configuration, access and permissioning, and related cloud resources
- Take end-to-end ownership rather than relying on separate infrastructure, architecture, or project-management teams to drive delivery
- Manage multiple client projects simultaneously while maintaining clear priorities, timelines, documentation, and follow-up
- Produce polished client-facing documentation, presentations, meeting notes, and project updates
- Collaborate with software and AI engineers on a proprietary platform deployed into customer cloud environments
- Contribute code and improvements to platform capabilities when needed
- Use AI tools extensively to accelerate engineering, research, analysis, and development while maintaining a strong understanding of the underlying technical decisions
Requirements
- 5+ years of professional, hands-on data engineering experience, ideally with significant senior-level ownership
- Advanced SQL skills
- Strong hands-on experience with at least one modern cloud data platform such as Snowflake, Databricks, BigQuery, or Redshift; experience across multiple platforms is highly valued
- Strong understanding of data architecture and data modeling principles, including common design patterns, anti-patterns, and architectural trade-offs
- Experience designing data environments across ingestion, compute, storage, transformation, and analytics/BI layers
- Hands-on cloud experience with AWS, GCP, and/or Azure beyond simply consuming cloud-hosted services
- Experience deploying or owning data platforms and working with infrastructure configuration, access controls, permissions, compute resources, and cloud architecture
- Experience making technical architecture decisions and clearly explaining the reasoning and trade-offs behind those decisions
- Excellent spoken and written English
- Strong client-facing or stakeholder-facing communication skills, with the confidence to lead technical discussions with senior stakeholders and executives
- Ability to communicate complex technical topics concisely and focus conversations on what matters
- Curiosity about AI and a strong understanding of how AI is changing modern data infrastructure, analytics, and data consumption
Preferred
- Significant hands-on experience with dbt
- Experience working as a Data Engineering Consultant, Data Consultant, Data Architect, Solutions Architect, or similar client-facing technical role
- Experience working in startups, consultancies, or other lean environments requiring broad technical ownership
- Experience owning or managing a complete data or analytics platform
- Experience across two or more of Snowflake, Databricks, BigQuery, and Redshift
- Strong understanding of cloud infrastructure, platform deployment, access management, and infrastructure cost considerations
- Previous software engineering experience before moving into data engineering
- Familiarity with emerging AI/data infrastructure concepts, including MCP and AI-oriented data workflows
Benefits
- Salary: $90K to 120K USD annually based on experience
- PTO: 15 days
- Holidays: Local or US holidays