Builds performant data processing pipelines, manages relational databases, and constructs cloud-portable data lake environments using modern open storage formats and embedded analytical engines.

Pipeline Design: Construct resilient data processing pipelines in Python using Medallion architecture principles (Bronze/Silver/Gold) to transform raw data into analytics-ready assets.

Open Storage Management: Standardize data storage using open formats including Parquet, Apache Iceberg, and Delta Lake to ensure cross-cloud compatibility.

Embedded Analytics: Leverage DuckDB integrated with Python for fast, lightweight, and performant localized data processing within ETL workflows.

Database Operations: Architect and optimize production PostgreSQL schemas while utilizing SQLite for embedded or lightweight localized storage needs.

Distributed Compute: Scale heavy ETL, data transformation, and analytics workloads using Databricks or Apache Spark.

Requirements

Advanced SQL proficiency with deep PostgreSQL expertise and working knowledge of SQLite.

Hands-on experience building robust analytical pipelines using Python and DuckDB.

Mastery of open table and storage formats (Parquet, Apache Iceberg, Delta Lake).

Proven track record designing data lakes using the Medallion data processing architecture.

Familiarity with the Databricks / PySpark ecosystem for distributed processing.

About AgileEngine

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

Why Join Us

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

About the Role

We are looking for a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer‑facing reporting, spend analytics, and AI features.

The mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper-intermediate English level.

What You Will Do

- Build and maintain the dbt pipelines, data marts, and warehouse models that serve in-app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
- Translate complicated procurement business logic into maintainable, well‑tested pipeline code, and notice when a model is valid‑looking but wrong.
- Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
- Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query, so that metrics and dimensions are defined once and answered consistently.
- Maintain the boundary between PII and agent/reporting-safe data.
- Build and maintain the FastAPI services that expose reporting marts to customers.
- Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
- Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
- Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
- Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
- Tune Snowflake performance and cost within the models you build.
- Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
- Share what you learn through code review and team discussion.
- Communicate clearly with engineering, product, and customer-facing teams about what the data means and what it can and can't do.

Must Haves

- 3+ years in analytics engineering, data engineering, or a closely related role, including hands‑on work on production data pipelines that other systems or customers depended on.
- Solid dbt experience: modeling, tests, macros, and incremental models.
- Strong SQL and working Python.
- Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
- Experience with a modern cloud warehouse (Snowflake preferred).
- Experience with ingestion tooling and orchestration (Airbyte, Prefect, or equivalents).
- Familiarity with agentic coding tools and an understanding of why guardrails around their output matter.
- Deep curiosity about the business: you want to understand what the data means and how customers use it, not just how it flows.
- Software engineering discipline for analytics code: Git, code review, CI/CD.
- Can take a multi-sprint piece of work from spec to production with normal oversight,



and can explain tradeoffs to technical and non-technical stakeholders.

Nice to Haves

- Experience building or maintaining API services on top of warehouse data (FastAPI or similar).
- Experience with a semantic layer or governed metric definitions that multiple consumers rely on, including for LLM-driven or agent-driven querying.
- Experience exposing warehouse data to customers through embedded analytics or customer-facing APIs.
- Experience with data observability tooling.
- Experience supporting customer data onboarding or implementation teams.
- Experience deploying data pipelines across multiple regions and managing PII or sensitive data.
- Knowledge of procurement and/or supply chain data models.
- Prior experience in enterprise software and startups.

Perks and Benefits

- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Versátil schedule with remote and office options.

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

Why Join Us

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

About the Role

We are looking for a Senior Data Engineer to operate and improve a Snowflake-based enterprise data platform in a regulated healthcare environment.

Must Haves

- 5+ years of professional experience in Data Engineering or Data Platform Engineering.
- Strong hands-on experience operating and developing solutions on Snowflake, including data-layer design, warehouse performance, access patterns, and production troubleshooting.
- Advanced SQL skills and strong experience with dbt for transformation, testing, documentation, lineage, and controlled deployment.
- Experience operating managed ingestion tools such as Fivetran or HVR and supporting custom data-ingestion pipelines.
- Hands-on experience with AWS data services, particularly S3 and event-driven or file-based ingestion patterns.
- Experience orchestrating and troubleshooting data workloads with Argo Workflows on Kubernetes, or comparable workflow-orchestration technologies.
- Proficiency in Python or a comparable language for data engineering, automation, and operational tooling.
- Strong understanding of data modeling, pipeline dependencies, schema evolution, backfills, data validation, and production data quality.
- Experience with observability, logging, alerting, and incident-management practices for production data platforms.
- Demonstrated ability to lead complex incident resolution, perform root-cause analysis, and convert findings into preventive improvements.
- Ability to make well-reasoned technical decisions, identify tradeoffs, estimate work, and guide improvements across a complex platform.
- Experience mentoring engineers and collaborating effectively with Cloud / DevOps, Analytics, Security, Governance, and business stakeholders.
- Strong written and verbal English communication skills, with the ability to work directly with client stakeholders.
- Full availability to work from 9:00 AM to 6:00 PM Pacific Time and participate in an agreed on-call rotation.

Nice to Haves

- Experience with large-scale data processing or streaming platforms.
- Experience designing reusable connector SDKs or integration frameworks.
- Understanding of identity-related security concepts such as authentication, authorization, credentials, service accounts, privileged access, and non-human identities.
- Experience with security events, audit logs, network telemetry, or cloud activity data.
- Familiarity with event-driven and distributed system architectures.
- Experience deploying AI/ML-enabled applications.
- Exposure to LLMs, AI agents, or AI-powered security applications.
- Solution Architecture or Forward Deployed Engineering experience.
- Previous experience in cybersecurity startups or high-growth SaaS environments.

What You Will Be Doing

- Design, develop, and maintain production-grade data collectors, connectors, and ingestion services.
- Integrate enterprise data sources including identity providers, cloud platforms, SASE solutions, network infrastructure, endpoints, applications, security platforms, and SIEM systems.
- Work directly with enterprise customers to understand their environments, data flows, APIs, security requirements, and integration constraints.
- Participate in technical onboarding processes, from initial discovery and architecture design through production deployment.




- Design scalable ingestion architectures for processing security events, logs, identity activity, network telemetry, and other high-volume data sources.
- Build integrations using REST APIs, cloud-native services, event-driven architectures, and modern software engineering practices.
- Develop backend services primarily using Python and/or Go.
- Work with SQL and NoSQL databases to store, transform, query, and analyze high-volume data.
- Build and operate containerized services using Docker and Kubernetes.
- Deploy and integrate solutions across AWS, Azure, and GCP environments.
- Troubleshoot complex customer deployment and integration issues across application, infrastructure, networking, API, and data layers.
- Optimize ingestion performance, reliability, scalability, and operational efficiency.
- Collaborate with Solution Architects, Sales Engineering, Customer Success, Product, and Engineering teams.
- Act as a trusted technical advisor around customer data architecture, collection strategies, security integrations, and implementation best practices.
- Identify recurring customer requirements and transform them into reusable connectors, platform capabilities, or improvements to the core product.
- Contribute technical insights from real-world implementations to the product roadmap.
- Support production adoption and continuously improve time-to-value for enterprise customers.
- Participate in occasional customer-facing technical sessions and, when necessary, onsite engagements.

What You Bring

- 10+ years of professional Software Engineering, Data Engineering, Platform Engineering, or related experience.
- Strong software development experience with:
- Python
- Go

- Strong experience designing and consuming REST APIs.
- Hands-on experience building data ingestion pipelines, connector frameworks, integration platforms, or similar systems.
- Experience integrating complex enterprise systems and third-party platforms.
- Solid understanding of both SQL and NoSQL databases.
- Production experience with at least one major cloud platform:

- AWS
- Azure
- GCP

- Experience working with Docker and Kubernetes.
- Strong understanding of Git-based development workflows and CI/CD practices.
- Experience designing reliable, scalable, and production-ready distributed systems.
- Strong debugging skills across APIs, infrastructure, networking, application services, and data pipelines.
- Experience working directly with customers, stakeholders, or technical partners.
- Ability to translate complex customer requirements into pragmatic technical solutions.
- Strong communication skills and the ability to operate effectively in customer-facing technical conversations.
- High degree of autonomy and ownership when solving ambiguous technical problems.

Highly Relevant Experience

Experience with any of the following will be especially valuable:

- Identity and Access Management (IAM)
- Identity Providers (IdPs)
- Identity Security
- Cybersecurity platforms
- SIEM systems
- Security telemetry
- Network security
- Endpoint security
- SASE / Zero Trust environments
- Cloud security
- Large-scale event processing
- Streaming architectures

Integration Experience

Previous experience integrating APIs or data from platforms such as the following is highly valuable:

- AWS
- Microsoft Azure
- Google Cloud
- Microsoft Entra ID / Active Directory
- Okta
- Google Workspace
- Duo
- CrowdStrike
- Zscaler
- Palo Alto
- Microsoft Defender
- SIEM and security analytics platforms

Direct experience with every platform is not required. What matters is a strong track record of learning complex APIs and transforming heterogeneous enterprise data sources into reliable integrations.

Nice to Have

- Experience with large-scale data processing or streaming platforms.
- Experience designing reusable connector SDKs or integration frameworks.
- Understanding of identity-related security concepts such as authentication, authorization, credentials, service accounts, privileged access, and non-human identities.
- Experience with security events, audit logs, network telemetry, or cloud activity data.
- Familiarity with event-driven and distributed system architectures.
- Experience deploying AI/ML-enabled applications.
- Exposure to LLMs, AI agents, or AI-powered security applications.
- Solution Architecture or Forward Deployed Engineering experience.
- Previous experience in cybersecurity startups or high-growth SaaS environments.

What Success Looks Like

Success in this role will be measured by your ability to:

- Deliver successful enterprise integrations and deployments.
- Reduce customer time-to-production.
- Build reliable and scalable ingestion solutions.
- Improve customer adoption and technical satisfaction.
- Resolve complex integration challenges efficiently.
- Turn customer-specific requirements into reusable platform capabilities.
- Contribute improvements that strengthen the scalability and flexibility of the core product.

What Darwoft Offers

- Contractor agreement with payment in USD
- 100% remote work
- Argentina's public holidays
- English classes
- Referral program
- Access to learning platforms

Explore this and other opportunities at:

#J-18808-Ljbffr

📌 Remote Ml & Data Analytics Engineer (Sql/Python) (Mendoza)
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