Senior data engineer building the automated marketing-reporting backbone for a performance marketing agency serving higher-ed and nonprofit clients: ingesting ad-platform and CRM data via Adverity into BigQuery, transforming it with dbt/Dataform, and powering Looker Studio dashboards and automated Google Sheets reports.
Compensation
Not disclosed
Location
Neuquén, Argentina
Work setup
Remote
Seniority
Not specified
Employment
Contract
Experience
5+ years
Category
Data engineering
Posted
Skills
active-directory
agentic-ai
ai
analytics
anomaly-detection
api
authentication
automation
aws
azure
backbone
bigquery
ci-cd
cloud
cloud-security
crm
crowdstrike
cybersecurity
data-engineering
data-ingestion
data-pipelines
data-quality
data-warehousing
dataform
dbt
distributed-systems
docker
entra-id
etl
fivetran
gcp
generative-ai
git
github
github-actions
google-ads
hubspot
iam
kubernetes
llm
looker
looker-studio
machine-learning
microsoft-defender
network-security
networking
nosql
observability
okta
predictive-analytics
python
rest
saas
sales-engineering
salesforce
sase
siem
sql
test-automation
zero-trust
zscaler
Must have
5+ years of hands-on data engineering experience
Experience configuring datastreams, transformations, and destinations in Adverity or a comparable ETL platform (Fivetran, Supermetrics, Funnel)
Intermediate to advanced SQL with proven BigQuery experience in transformation, partitioning, and modeling
Expertise in dbt or Dataform for modular, version-controlled, documented SQL transformation pipelines
Python proficiency for custom API connections and pipeline automation
Experience with CRM data flows and integrations (Slate, Salesforce, and/or HubSpot)
Deep understanding of marketing metrics (ROAS, CPA, CAC) and paid platform schemas
Track record of structuring data to optimize Looker Studio performance
Proficiency with Git and collaborative development workflows
Ability to manage multi-client data environments with strict data segregation
Strong communication skills translating complex data workflows for non-technical audiences
Nice to have
Data engineering experience in a marketing or agency environment
Education marketing experience (traditional or online)
Experience integrating AI/ML models into marketing data pipelines for predictive analytics
Familiarity with AI/LLM tools for forecasting, audience segmentation, or marketing workflows
Full description
BigQuery
Data Engineer (Marketing-oriented) | Remote for Marketing Agency for Education
Our client is a fast-growing performance marketing agency specializing in mission-driven organizations across higher education and nonprofit sectors. Since launching in 2020, the agency has built a strong reputation for combining strategic thinking, creative execution, and data-driven marketing to help universities and organizations achieve measurable growth.
Company Overview:
Our client is a fast-growing performance marketing agency specializing in mission-driven organizations across higher education and nonprofit sectors. Since launching in 2020, the agency has built a strong reputation for combining strategic thinking, creative execution, and data-driven marketing to help universities and organizations achieve measurable growth.
Your Role:
The Senior Data Engineer sits at the center of the agency's Data and Analytics function, connecting every paid media platform and CRM the agency touches into a single, reliable source of truth. You'll own the full path data takes: from a client's ad accounts and CRM, through Adverity, into a BigQuery warehouse, through modular dbt/Dataform transformations, and out to the Looker Studio dashboards and automated Google Sheets reports the team and its partners rely on daily. Reporting to the Data and Analytics Manager, you're joining at a pivotal moment — the agency is scaling its roster of education and nonprofit partners, and this role is instrumental in evolving the automated reporting suite that makes that growth possible without sacrificing data quality or client trust.
You’ll:
Own data ingestion & pipeline architecture — connect and maintain API pipelines from paid media platforms (Meta, Google Ads, LinkedIn, TikTok, programmatic) into Adverity, build custom Python connectors for anything not natively supported, and architect scalable pipelines from source system through to dashboard
Own CRM integration — build and maintain reliable CRM data connections (Slate, Salesforce, HubSpot) so lead and enrollment data flows cleanly into the warehouse
Own the data warehouse — route, optimize, and store raw and processed data in Google BigQuery; design efficient schemas for complex, multi-source marketing data (granularity, normalization, slowly changing dimensions); manage partitioning, clustering, and storage strategy to control query cost and performance
Own transformation & mapping — build SQL schemas that map, blend, and organize multi-channel data accurately for each client, in modular, version-controlled, documented dbt or Dataform pipelines, with standardized naming conventions and metric definitions across platforms and clients
Data Engineer - Automated Pipelines & Analytics at Bairesdev (Remote) — aijobsearch
Own CI/CD & data segregation — maintain CI/CD pipelines (GitHub Actions, automated testing) for robust, version-controlled releases, and enforce strict data segregation across multi-client environments
Enable the reporting layer — create clean, performant data views built specifically for fast Looker Studio dashboards and automated Google Sheets reports, and partner with the Data and Analytics Manager and client teams to translate reporting requirements into warehouse models
Drive data quality — monitor pipeline health, resolve API breaks, minimize latency, implement validation frameworks and automated testing, troubleshoot discrepancies to root cause across source/warehouse/BI layers, and proactively flag data quality risks to leadership
You Bring:
5+ years of hands‑on data engineering experience, ideally in a marketing or agency environment
Strong experience configuring datastreams, transformations, and destinations in Adverity or a comparable ETL platform (Fivetran, Supermetrics, Funnel)
Intermediate to advanced SQL, with proven BigQuery experience in transformation, partitioning, and modeling
Expertise in dbt or Dataform, managing modular, version‑controlled, documented SQL transformation pipelines
Python proficiency for custom API connections and pipeline automation
Experience with CRM data flows and integrations (Slate, Salesforce, and/or HubSpot)
A deep understanding of marketing metrics (ROAS, CPA, CAC) and paid platform schemas, with a proven track record of structuring data to optimize Looker Studio performance
Proficiency with Git and collaborative development workflows, and proven ability to manage multi‑client data environments with strict data segregation
An analytical, solution‑driven mindset with a growth orientation, and strong communication skills that turn complex data workflows into clear narratives for non‑technical audiences — education marketing experience (traditional or online) is especially valued
Bonus Points:
Experience integrating AI‑driven insights or machine learning models into marketing data pipelines to sharpen predictive analytics and data utility
Familiarity using AI or LLM tools to optimize performance forecasting, automated audience segmentation, or marketing workflows
Experience connecting, managing, and maintaining APIs for generative AI platforms within a marketing data architecture
Experience using AI/ML‑driven anomaly detection to monitor pipelines and proactively flag data quality issues
What’s Offered:
Remote role with the flexibility that comes with a distributed, async‑friendly team
Competitive compensation based on experience and location
A pivotal, high‑visibility seat on a scaling Data & Analytics team, with real ownership over the agency's reporting infrastructure
The chance to build the data backbone for an agency whose partners are education and nonprofit organizations working to build a better world
A culture built around finding a better way, playing as a team of A‑players, and treating results as the engine rather than the goal
Direct impact: the pipelines and models you build directly shape how partners see (and trust) their own performance data
Interview Process:
Application Review (resume + a few questions)
Screening Call with Atomic HR
Hiring Team Interview (walkthrough of a pipeline or data model you've built)
Technical Assessment (a real‑world data engineering case study)
At Darwoft, we build custom software solutions and partner with international companies to create high‑impact digital products.
We are a Latin American technology company with a people‑first culture, strong professional standards, and a long‑term partnership mindset. We work with distributed teams, complex technical challenges, and clients whose technology has a direct impact on their businesses and users.
About the Project
We are partnering with an innovative cybersecurity company building a next‑generation Identity Security and Identity Observability platform.
The product helps enterprise security teams understand how human users, service accounts, workloads, applications, and AI‑driven identities access systems across cloud, SaaS, network, endpoint, and on‑premise environments.
At the core of the platform is a highly data‑intensive architecture that collects and analyzes large volumes of events, logs, network telemetry, identity activity, and security signals. This information is processed to identify unusual access patterns, security risks, identity blind spots, infrastructure misconfigurations, and potential threats.
The platform integrates with complex enterprise ecosystems including identity providers, cloud platforms, SASE solutions, SIEMs, endpoint security tools, network infrastructure, and security services.
About the Role
We're looking for a Senior Data Engineer focused on Data Ingestion, Integrations, and Collectors to become a key technical bridge between enterprise customers and the engineering organization.
This is not a traditional backend or internal data engineering position.
You'll work directly with customers to understand their infrastructure, data sources, security architecture, network topology, APIs, and workflows, then design and implement scalable mechanisms to ingest that information into the platform.
You’ll combine data engineering, software engineering, solution architecture, cloud infrastructure, and technical consulting.
A major part of your mission will be building and evolving connectors and ingestion pipelines capable of integrating data from complex enterprise environments while maintaining reliability, scalability, security, and fast time‑to‑production.
What You’ll 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
Deliver successful enterprise integrations and deployments.
Reduce customer time‑to‑production.
Build reliable and scalable ingestion solutions.
Improve customer adoption and technical satisfaction.