Hire Influxdb Developers
Access Influxdb Developers from LatAm. Specialists in time-series databases, data ingestion pipelines, and performance tuning ready in 21 days.














Hire Remote Influxdb Developers


Sofía is a dynamic developer from Colombia, mastering JS, React, and Docker for 5 years.
- JavaScript
- HTML
- React.js
- TypeScript
- Docker


Meet Benjamín, your go-to developer with 12 years of Vue.js, AWS, and SQL expertise.
- Vue.js
- TypeScript
- Node.js
- AWS
- SQL


Meet Miguel: A developer with 10 years of experience turning code into solutions.
- Ruby
- Data Visualization
- Python
- C++
- Docker


João is a skilled developer from Brazil, mastering Python, APIs, and SQL with flair.
- Python
- Machine Learning Basics
- CSS
- APIs
- SQL


Diego is a seasoned developer from Mexico, mastering Go, Node.js, React, and AWS.
- Go (Golang)
- Node.js
- HTML
- React.js
- AWS


Camila is a developer from Argentina, crafting digital solutions with 6 years of expertise.
- PHP
- CSS
- SQL
- APIs
- JavaScript


Mateo is a charismatic developer with 12 years of crafting code and building solutions.
- Java
- Spring Boot
- C++
- APIs
- AWS

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Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
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We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Reviews

"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."


“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”


“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”


“We scaled our first tech team at record speed with Lupa. We couldn’t be happier with the service and the candidates we were sent.”

"Recruiting used to be a challenge, but Lupa transformed everything. Their professional, agile team delivers top-quality candidates, understands our needs, and provides exceptional personalized service. Highly recommended!"


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"We hired 2 of our key initial developers with Lupa. The consultation was very helpful, the candidates were great and the process has been super fluid. We're already planning to do our next batch of hiring with Lupa. 5 stars."

"Working with Lupa for LatAm hiring has been fantastic. They found us a highly skilled candidate at a better rate than our previous staffing company. The fit is perfect, and we’re excited to collaborate on more roles."


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Working with Lupa was a great experience. We struggled to find software engineers with a specific skill set in the US, but Lupa helped us refine the role and articulate our needs. Their strategic approach made all the difference in finding the right person. Highly recommend!

Lupa goes beyond typical headhunters. They helped me craft the role, refine the interview process, and even navigate international payroll. I felt truly supported—and I’m thrilled with the person I hired. What stood out most was their responsiveness and the thoughtful, consultative approach they brought.

Influxdb Developers Soft Skills
Problem Solving
Analyze large time-series datasets for operational and business insights.
Adaptability
Handle changes in schema, data sources, and performance demands.
Communication
Translate analytics results into actionable strategies for stakeholders.
Collaboration
Work with engineers to fine-tune data pipelines and visualizations.
Attention to Detail
Validate data accuracy before reporting and system updates.
Curiosity
Investigate novel analytics and monitoring approaches for time-series data.
Influxdb Developers Skills
Time-Series Database Management
Design and maintain Influxdb instances optimized for large-scale data ingestion.
Query Optimization
Implement efficient queries for high-performance time-series analytics.
Data Retention Policies
Configure retention rules to manage storage and maintain data relevance.
Integration Pipelines
Connect Influxdb to visualization tools and monitoring systems.
Security Controls
Apply access control and encryption to safeguard time-series data.
How to Write an Effective Job Post to Hire Influxdb Developers
Recommended Titles
- InfluxDB Database Developer
- Time-Series Data Engineer
- InfluxDB Query & Optimization Specialist
- IoT Data Pipeline Developer – InfluxDB
- Data Storage & Retrieval Engineer
- InfluxDB Cloud Developer
Role Overview
- Tech Stack: Proficient in InfluxDB, Telegraf, Grafana, and time-series data processing.
- Project Scope: Design, optimize, and maintain time-series databases for real-time analytics.
- Team Size: Collaborate with DevOps engineers, backend developers, and data analysts (4–6 members).
Role Requirements
- Years of Experience: Minimum of 3 years working with time-series databases.
- Core Skills: Query optimization, retention policy design, high-ingest data pipelines.
- Must-Have Technologies: InfluxDB, Flux, Telegraf, Grafana, Docker.
Role Benefits
- Salary Range: $95,000 – $140,000 depending on data engineering expertise.
- Remote Options: Fully remote with flexible scheduling.
- Growth Opportunities: Work on high-volume, mission-critical monitoring and analytics systems.
Do
- Show mastery in InfluxDB for time-series data at scale
- Include work on real-time monitoring, alerts, and dashboards
- Highlight performance tuning for high-ingest environments
- Use precision-focused, data-driven job language
Don't
- Don’t confuse InfluxDB with general SQL databases
- Avoid omitting time-series data requirements
- Don’t ignore retention policy configurations
- Steer clear of vague performance tuning expectations
- Don’t skip integration with monitoring tools
Top Influxdb Developers Interview Questions
InfluxDB Developer questions to test database skill
What’s your experience with InfluxDB time-series data?
Look for understanding of retention policies, continuous queries, and schema design tailored for high-ingest scenarios.
How do you optimize InfluxDB queries?
Expect mention of tag usage, measurement partitioning, and query profiling with InfluxQL or Flux.
What’s your approach to scaling InfluxDB?
Look for experience with clustering, shard management, and handling large datasets efficiently.
How do you integrate InfluxDB with visualization tools?
Expect examples with Grafana or Chronograf, including alert configuration and dashboard optimization.
Describe a real-world time-series solution you’ve built.
Look for clarity on ingestion, transformation, storage, and insights derived from the data.
How would you troubleshoot slow queries in InfluxDB?
Look for index usage analysis, query optimization, and shard duration tuning.
What’s your approach to handling high cardinality data?
Expect retention policy adjustments, downsampling, and tag value optimization.
How do you recover from data corruption in a time-series database?
Look for backups, WAL recovery, and cluster replication strategies.
How would you diagnose write performance bottlenecks?
Expect disk I/O analysis, batch writes, and concurrent write tuning.
How do you handle retention and continuous queries efficiently?
Look for scheduled tasks, measurement aggregation, and resource allocation.
Tell me about a time you redesigned a schema for better query performance.
Look for strong reasoning on measurement naming, tags vs. fields, and retention policy decisions that improved speed or reduced cost.
Describe how you handled a critical data loss or corruption incident.
Expect a systematic approach with backups, WAL recovery, and preventative measures implemented afterward.
When have you optimized continuous queries or downsampling for efficiency?
Look for practical trade-offs between resolution, storage costs, and analytical needs.
How did you resolve a situation where a dashboard was showing inaccurate metrics?
Expect debugging across data ingestion, query logic, and visualization layers.
Share an example of teaching non-technical staff to interpret time-series data.
Look for communication clarity and creation of easy-to-use tools or documentation.
- Weak grasp of tags vs. fields and cardinality impact
- Ignores retention policies and downsampling strategy
- Overuses GROUP BY time() without window logic
- Neglects write batching and shard duration tuning
- Poor backup/restore and WAL recovery practices

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From search to hire, our process is designed to secure the perfect talent for your team

Local Expertise
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Lupa will help you hire top talent in Latin America.
Book a Free ConsultationTop Influxdb Developers Interview Questions
InfluxDB Developer questions to test database skill
What’s your experience with InfluxDB time-series data?
Look for understanding of retention policies, continuous queries, and schema design tailored for high-ingest scenarios.
How do you optimize InfluxDB queries?
Expect mention of tag usage, measurement partitioning, and query profiling with InfluxQL or Flux.
What’s your approach to scaling InfluxDB?
Look for experience with clustering, shard management, and handling large datasets efficiently.
How do you integrate InfluxDB with visualization tools?
Expect examples with Grafana or Chronograf, including alert configuration and dashboard optimization.
Describe a real-world time-series solution you’ve built.
Look for clarity on ingestion, transformation, storage, and insights derived from the data.
How would you troubleshoot slow queries in InfluxDB?
Look for index usage analysis, query optimization, and shard duration tuning.
What’s your approach to handling high cardinality data?
Expect retention policy adjustments, downsampling, and tag value optimization.
How do you recover from data corruption in a time-series database?
Look for backups, WAL recovery, and cluster replication strategies.
How would you diagnose write performance bottlenecks?
Expect disk I/O analysis, batch writes, and concurrent write tuning.
How do you handle retention and continuous queries efficiently?
Look for scheduled tasks, measurement aggregation, and resource allocation.
Tell me about a time you redesigned a schema for better query performance.
Look for strong reasoning on measurement naming, tags vs. fields, and retention policy decisions that improved speed or reduced cost.
Describe how you handled a critical data loss or corruption incident.
Expect a systematic approach with backups, WAL recovery, and preventative measures implemented afterward.
When have you optimized continuous queries or downsampling for efficiency?
Look for practical trade-offs between resolution, storage costs, and analytical needs.
How did you resolve a situation where a dashboard was showing inaccurate metrics?
Expect debugging across data ingestion, query logic, and visualization layers.
Share an example of teaching non-technical staff to interpret time-series data.
Look for communication clarity and creation of easy-to-use tools or documentation.
- Weak grasp of tags vs. fields and cardinality impact
- Ignores retention policies and downsampling strategy
- Overuses GROUP BY time() without window logic
- Neglects write batching and shard duration tuning
- Poor backup/restore and WAL recovery practices