Caching infrastructure engineer

A caching infrastructure engineer, known in French as an ingénieur infrastructure de cache, designs and operates a distributed caching service for applications. This service stores copies of data from an authoritative source to reduce latency, meaning response time, and handle load. The specialist considers how fresh these copies are and how applications behave when the cache responds poorly or becomes unavailable.

Their work covers the shared service, the client libraries that allow applications to use it and the invalidation mechanisms. Invalidating data means removing or making unusable a copy that has become stale following a change in the source. The engineer must also handle concurrent access: a read that starts before a change can put an old value back into the cache after it has been invalidated.

The responsibility therefore goes beyond adding Redis to an application. It concerns the relationship between source data, shared copies and application clients. The organisation must clarify who decides on freshness rules, who changes the clients and who responds to an incident. To establish reporting lines, identify the person technically responsible for the service and the teams whose applications depend on it, rather than inferring authority from the job title alone.

Caching infrastructure engineer, backend developer and database administrator: who does what?

• Caching infrastructure engineer: assign them responsibility for designing and operating the caching layer, its interfaces and its consistency with the sources.
• Backend developer: clarify their responsibility for the application code that reads and changes data, in coordination with the caching specialist.
• Database administrator: distinguish their responsibility for the source database from decisions about cached copies.

This allocation helps define the role without requiring three separate teams. This profile covers distributed application caching. It excludes general database administration, operating the entire platform and build caching.

A cache must speed up access to data without causing errors when that data is used. Recruitment must therefore clarify several related decisions: which data to copy, how long to retain it and how to respond to a change in the source. Faster response times are not enough to judge the quality of the service if applications read stale values.

The lifetime, or TTL for « time to live », determines how long a copy can remain in the cache. The choice depends on changes in the source and the freshness the application requires. Eviction addresses a different question: which data should be removed when the available memory is no longer sufficient? Confusing expiration with eviction risks producing a configuration that addresses capacity without addressing freshness.

Availability must be assessed alongside that of the source database. When a cache is lost, applications may fall back to reading from that database. This behaviour must be tested and fallback reads limited to avoid overloading it. Amazon also describes coalescing simultaneous requests for the same missing data to reduce bursts of reads.

Hypothetical example: several applications share a cache of product data. Following a change in the database, a read already in progress puts an old value back into the cache. The technical lead needs to understand how the candidate would detect and handle this sequence, beyond simply adjusting the time to live.

Someone familiar only with integrating a cache into an application may overlook client reconnections, format changes or failure tests. Conversely, hiring a specialist without giving them access to the teams that change the data limits their ability to handle invalidation. Before choosing a candidate, clarify which decisions they will be able to make about the service and which will require agreement with the teams using it.

• Design the distributed caching service around application reads, expected latency and expected load.
• Define expiration and invalidation rules based on changes in the source and the freshness applications need.
• Develop or adapt client libraries that handle connections, timeouts, reconnections and errors.
• Handle concurrent access to prevent an earlier read from reintroducing a stale value after invalidation.
• Size the memory and choose an eviction policy suited to the cache’s capacity.
• Monitor cache hits and misses, memory and CPU usage, and the load on the source database.
• Test cache unavailability and limit reads that fall back to the source database.
• Prepare changes to cached data formats to maintain compatibility during deployments.
• Protect the cache’s network communications with appropriate encryption mechanisms.

Look for a grounding in programming, concurrency and communication between machines. The candidate must understand what happens when several operations run at the same time and when a network response is delayed or fails. An application-focused background can provide these foundations, provided it has involved work on the behaviour of the cache and its clients.

Cnam’s « Programmation système et répartie » module covers Linux processes, threads, synchronisation, sockets and remote procedure calls, among other topics. Threads are strands of execution within a process. Sockets enable network communication. These topics can build useful knowledge without constituting a mandatory pathway into this role.

Redis University also offers courses and exercises on Redis, its operation, availability and observability, meaning monitoring the service’s behaviour through measurements. This training complements practical experience. A certificate of successful completion does not prove that the candidate has already been responsible for a production service. The former Redis Developer certification was retired in June 2024: avoid making it a current requirement in the job description.

To assess experience, distinguish the responsibilities the candidate has held. Using a cache in an application, adapting a client library and operating a shared service do not involve the same decisions. Ask which part the candidate designed, which changes they had authority to decide on and how they contributed to incident handling. A degree alone cannot establish this autonomy. Match expectations to the decisions they will actually be entrusted with on the cache, without imposing a universal length of experience.

To assess this profile, connect the role’s responsibilities to explained past work, a practical exercise and discussions with the relevant technical stakeholders.

1. Define the criteria before the interview

Build an assessment framework around data consistency, client behaviour, cache capacity and failure handling. Include coordination with application teams.

For each criterion, specify the decision the candidate will need to make in your organisation. Distinguish what they must be able to handle independently from what they can prepare with another specialist.

If your company lacks expertise in distributed caching, have an expert in these systems assess the technical reasoning. Leave the assessment of the expected responsibilities and cooperation to the hiring manager.

2. Examine past work

Ask the candidate to describe a service they contributed to, from the data source through to the client applications. Have them clarify their personal contribution and the decisions made by others.

Invite them to explain an expiration or invalidation rule, then an incident that called that rule into question. Ask which measurements helped identify the problem.

A positive sign is an explanation that connects decisions to application constraints. Pay attention to answers that merely name a product or attribute all the work to the group.

3. Explore their reasoning on a scenario relevant to the role

Hypothetical example: a database read starts, then the data changes and its copy is invalidated. The first read subsequently completes and puts the old value back into the cache.

Ask the candidate to map out the sequence of operations and identify the risk. Invite them to propose a solution and explain the conditions under which it works.

Then introduce a loss of the cache and ask how the clients respond. Have them clarify how reads to the database are limited and which measurements would help monitor the situation.

Adapt the scenario to the mechanisms actually used. If the clients have a local Redis cache, examine the loss of their invalidation channel and the necessary clearing of local copies separately.

Value explicit assumptions and proposed tests. An answer that promises total availability without discussing dependencies deserves further exploration.

4. Examine operations and changes

Ask how the candidate would distinguish memory saturation, cache misses and increased load on the source. Have them explain the differences between expiration and eviction.

Present a change to the data format and ask how to maintain compatibility during deployment. Also examine client timeouts, reconnections and error handling.

If your service uses Sentinel, ask how clients handle failover. Have them explain the limits of asynchronous replication, which does not guarantee that all acknowledged writes are retained.

5. Assess cooperation and confirm responsibilities

Ask the candidate to explain a trade-off between freshness and response time to a product lead. Observe whether they identify the effects on usage and the decisions that need to be shared.

If the role includes management, assess their people management experience separately. For an expert role, focus on coordinating changes with developers and those responsible for the database.

When taking references with the candidate’s authorisation, look for evidence that corroborates the responsibilities described and how they handle incidents. Compare this feedback with the work they have presented, without replacing technical analysis with a general assessment.