Hire Influxdb Developers

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

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Hire Remote Influxdb Developers

Sofía G
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5 years of experience
Part-Time

Sofía is a dynamic developer from Colombia, mastering JS, React, and Docker for 5 years.

Skills
  • JavaScript
  • HTML
  • React.js
  • TypeScript
  • Docker
Benjamín S
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12 years of experience
Part-Time

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

Skills
  • Vue.js
  • TypeScript
  • Node.js
  • AWS
  • SQL
Miguel C
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10 years of experience
Full-Time

Meet Miguel: A developer with 10 years of experience turning code into solutions.

Skills
  • Ruby
  • Data Visualization
  • Python
  • C++
  • Docker
João S
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5 years of experience
Full-Time

João is a skilled developer from Brazil, mastering Python, APIs, and SQL with flair.

Skills
  • Python
  • Machine Learning Basics
  • CSS
  • APIs
  • SQL
Diego L
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12 years of experience
Full-Time

Diego is a seasoned developer from Mexico, mastering Go, Node.js, React, and AWS.

Skills
  • Go (Golang)
  • Node.js
  • HTML
  • React.js
  • AWS
Camila F
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6 years of experience
Part-Time

Camila is a developer from Argentina, crafting digital solutions with 6 years of expertise.

Skills
  • PHP
  • CSS
  • SQL
  • APIs
  • JavaScript
Mateo G
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12 years of experience
Full-Time

Mateo is a charismatic developer with 12 years of crafting code and building solutions.

Skills
  • Java
  • Spring Boot
  • C++
  • APIs
  • AWS
Ana M
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7 years of experience
Full-Time

Ana is a dynamic developer from Panama, blending AI and Python with 7 years of expertise.

Skills
  • C++
  • Machine Learning Basics
  • Data Visualization
  • AI
  • Python
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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."

RaeAnn Daly
Vice President of Customer Success, Blazeo

“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”

Phillip Gutheim
Head of Product, Rappi Bank

“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”

Dan Berzansky
CEO, Oneteam 360

“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.”

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CEO, Bacu

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Accounting and Finance Director, Pasos al Éxito

“Lupa has become more than just a provider; it’s a true ally for Pirani in recruitment processes. The team is always available to support and deliver the best service. Additionally, I believe they offer highly competitive rates and service within the market.”

Tania Oquendo Henao
Head of People, Pirani

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VP of Revenue, Komet Sales

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John Vanko
CTO, GymOwners

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Daniel Ruiz
Head of Engineering, Fuse Finance

"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."

Joaquin Oliva
Co-Founder, EBI

"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."

Kim Heger
Chief Talent Officer, Hakkoda

"We compared Lupa with another LatAm headhunter we found through Google, and Lupa delivered a far superior experience. Their consultative approach stood out, and the quality of their candidates was superior. I've hired through Lupa for both of my companies and look forward to building more of my LatAm team with their support."

Josh Berzansky
CEO, Proven Promotions & Vorgee USA

“We’ve worked with Lupa on multiple roles, and they’ve delivered time and again. From sourcing an incredible Senior FullStack Developer to supporting our broader hiring needs, their team has been proactive, kind, and incredibly easy to work with. It really feels like we’ve gained a trusted partner in hiring.”

Jeannine LeBeau
Director of People and Operations, Intevity

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!

Mike Bohlander
CTO and Co-Founder, Outgo

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.

Matt Clifford
Founder, Matt B. Clifford Consulting

Influxdb Developers Soft Skills

Analytical discipline and time-series fluency that turn raw metrics into clear insight

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 data expertise that drives real-time analytics and insights

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

This is an example job post, including a sample salary expectation. Customize it to better suit your needs, budget, and attract top candidates.

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

Why We Stand Out From Other Recruiting Firms

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

Local Expertise

Tap into our knowledge of the LatAm market to secure the best talent at competitive, local rates. We know where to look, who to hire, and how to meet your needs precisely.

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Retain complete control over your hiring process. With our strategic insights, you’ll know exactly where to find top talent, who to hire, and what to offer for a perfect match.

Seamless Compliance

We manage contracts, tax laws, and labor regulations, offering a worry-free recruitment experience tailored to your business needs, free of hidden costs and surprises.

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Joseph Burns
Founder

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

Frequently Asked Questions

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