Support Engineer

The successful candidate will play a key role in our dynamic and award-winning company. You’ll be responsible for providing customers with hands on engineering support for CS developed software. This will be to mobile network operators, law enforcement and emergency service organisations worldwide.

What are the main job responsibilities?

  • To help ensure that we comply with SLA obligations towards customers, and follow up internally with the appropriate teams to that end.
  • To record, recreate, investigate, and resolve or escalate issues raised by our customers.
  • Observe and help develop best practice within the company for all support issues.
  • To routinely follow documented procedures to ensure the health of our deployed customer solutions.

2nd Line Support

  • Remote troubleshooting and problem analysis on systems deployed to a Linux environment in our customer environments.
  • Proactively monitoring and verifying the deployed solutions and their behaviour.
  • Routine customer communication to track issues, liaising directly with customers and in-country partners.
  • Conducting customer solution health checks and producing reports on these.
  • Taking support calls from customer technical teams via phone, email or ticketing system.
  • Taking responsibility for the above and following to resolution.
  • Reproducing issues or bugs in local environments and creating Jira issue reports.
  • Providing customers with advice, guidelines, and / or documentation.
  • Some configuration of in-house software.
  • Assistance with analysis of test results from customer deployments.
  • Post deployment / upgrade health checks.
  • Covering out of hours customer contact if required.

What is the expected minimum level of experience required for this role?

  • A BSc minimum is required, preferably Software Eng, Telecom Eng, or Computer Science and equivalent to UK 2:1
  • We are looking for Level 2 Support Engineer with at Linux, networking and database skills.
  • The person will also have good experience of customer facing roles and provable problem-solving experience in a complex software environment.
  • The person will be highly articulate and numerate and will have experience of supporting similarly complex projects.
  • Hands on L2 or L3 experience from a support role in Telecoms, Finance – or any other industry where there are mission critical systems.
  • Unlikely to meet our requirements if they do not have experience commensurate with at least 3 years in a technical role with similar responsibilities. 

Candidates needs to be:

  • Bright, Dynamic and Reliable.
  • Experienced with Linux.
  • Experienced with networking
  • Experienced with SQL preferably with MySQL or Postgres.
  • Able to write excellent quality technical documentation such as: user guides, operation manuals, etc. in English.
  • Able to communicate and liaise with customers whose first language is not English at a technical level.

Building the Foundation for Mobile Network Data Monetisation in the 5G Era

Mobile Network Operators need the capability to exploit their network data effectively and efficiently in order to support their digital transformation goals, make insightful business decisions and capitalise on the 5G opportunity. This will help drive innovation, create new revenue streams, optimise the customer experience, build loyalty, reduce costs and much more.

However, getting quality actionable data as and when required can be challenging, extremely costly and very time consuming. The raw network data is usually inadequate for business intelligence purposes and very complex – distributed within the network, in various formats, from multiple system vendors, constantly changing, vast and ever growing with the advent of 5G and IoT.

Business units need the data to be consolidated, transformed, enriched and adequately formatted for various analytics platforms – without compromising the integrity of the data and without undue delays or disruptions. This enables the insights required to be obtained when and as required to support the decision-making process.

The quality of the data is of utmost importance, as erroneous decisions made from bad data can be extremely costly. According to IBM, the US economy loses $3.1 trillion annually due to poor data quality. Considering the particular complexity, volume and velocity of mobile network data, this issue is exacerbated for MNOs looking to capitalise on mobile network data.

Inflexible Legacy Solutions

Some of the solutions mobile operators have implemented to enable data intelligence are often inflexible, requiring for example an extensive reconfiguration of the operator’s environment to make changes or add a new analytics solution; this can be very costly, excessively time consuming and risky.

Operators have also complained about not being able to get the quality of data they need. All of this hinders innovation and results in mobile network operators missing out on opportunities to monetise mobile network data and optimise the business.

The Three Pillars of Effective Data Enablement

1 – Transforming Raw Network Data into Quality Actionable Data

MNOs need a powerful data ingestion, governance and management capability that enables complex raw network data from distributed sources and in various formats to be easily collated and manipulated in order to provide actionable data as and when needed. The solution should be able to:

  • Extract the data from the various silos, feeds and networks (5G, 4G, 3G, 2G) spread across the ecosystem
  • Ingest, transform, correlate and enrich the data as required, making all the information usable for both streaming and storage for future use. Predictive analytics, for example, rely on historical data to predict trends – the longer the history, the better the predictability
  • Filter and format billions of records per day into the correct format for each application and protect them from downstream changes going forward
  • Maintain the integrity and quality of the data throughout the transformation process
  • Support data governance with privacy, security and compliance functions

2 – Delivering Fast Time to Actionable Data, Cost-Effectively

More than ever, MNOs need to be more agile, able to respond to changing situations promptly, seize market opportunities and innovate rapidly in order to thrive in a very competitive market. Relying on huge and complex systems to use and analyse network data hinders innovation and time to market.

What is required are data enablement solutions that are extremely flexible and easy to integrate, giving MNOs the freedom to make changes and introduce new analytics/OSS solutions rapidly and effectively, without creating chaos and unreasonable costs:

  • Cloud ready, able to be delivered as a software service within a cloud (public or private) infrastructure. This equates to faster deployment times, ease of scalability and high availability.
  • Cloud native, with established interconnectivity with public cloud service providers such as Microsoft Azure, Google Cloud and Amazon AWS. This ensures that large volumes of data can be transferred efficiently (and hence cost-effectively) into cloud infrastructures.
  • Able to work alongside existing solutions and not requiring a complete re-architecting of an MNO’s environment.
  • Non-proprietary, an open software platform that is compliant with industry standards (e.g. 3GPP, TM Forum, OSSii).
  • Independent of network equipment vendors, OSS vendors and analytics solutions.
  • Enabling changes to be managed within the solution, not on the MNO’s analytics/application layer, which makes it invisible to the application users and ensures business continuity.
  • Highly scalable, able to support the exponential growth of network data driven by 5G and IoT (IDC estimates that the number of IoT devices will reach 41.6 billion by 2025).

Such agility will help MNOs to accelerate insights-driven business decisions, innovate and create new revenue streams with minimum disruption, risk and cost.

3 – Providing Accurate Mobile Location Intelligence

Location is one of the most valuable types of network data that is highly coveted by all. It is used by operators internally for geo marketing and commercial purposes, but it is also extremely desirable for external businesses and governmental agencies. Smart cities, for instance, rely on location information to analyse population movement patterns for planning purposes, in order to optimise infrastructures and minimise the impact on the environment. Advertisers want location insights to launch their ad campaigns in the right place at the right time to maximise impact. Governments also need location information to enable emergency services and law enforcement.

5G brings the potential for higher accuracy location faster, which can power many innovative use cases. Nevertheless, obtaining accurate and fast mobile location consistently remains a challenge on all networks and MNOs need the right technologies and expertise to help:

  • The capacity to deliver high accuracy location reliably and fast to enable more use cases. Typically, cell level of accuracy ranges from 200–500m in urban environments to 1–10km in rural areas – which may be enough in some cases, but would be inadequate for many commercial, emergency services and law enforcement requirements. For example, much higher accuracy is required to gain location insights around a billboard or to distinguish between a customer coming into a mall or one driving along a nearby motorway.
  • A flexible, adaptable solution with full passive and active capabilities that can deliver real-time, historical and mass location intelligence for both commercial and regulatory purposes.
  • A reliable network-based solution that enables MNOs to profit from their network location data by providing accurate and non-intrusive location intelligence, with nothing needing to be installed on the device.
  • A solution that works on any network technology (5G, 4G, 3G, 2G) and their MVNOs, for any mobile phone (not just smartphones) and IoT devices, anytime and anywhere – even in dense urban environments, indoors or outdoors, in-country or overseas.

With the right solution, MNOs will be able to leverage network location data to differentiate and further take advantage of 5G, which can support higher levels of accuracy.

By meeting the requirements for effective, agile data enablement, mobile network operators will be able to turn raw, complex and distributed data into usable, profitable assets – fast.  This will lay the foundation for mobile network data monetisation in the 5G era. Some operators have already risen to the challenge and are forging ahead.

Article originally published on Telecoms.com, 31 January 2022

Author: Corine Suscens, VP Global Marketing, Creativity Software – With 18 years in Technology/Telecom marketing, Corine Suscens has been developing leading edge thought leadership content for the industry. During her career, Corine has helped leading companies to explain their technical offerings in order to maximise industry understanding. She has written several whitepapers tackling key business challenges that operators have been facing. Corine currently leads the marketing department at Creativity Software, supporting the company’s growth and developing thought leadership marketing globally. Corine holds a Masters in Management from Grenoble Ecole de Management.

The location intelligence challenge of ensuring public safety

As public safety agencies are striving to gain location intelligence to fulfil their missions to safeguard the public, location analytics is becoming increasingly important. The demands for greater planning and faster reaction to public safety events are being driven by governments and organizations like the European Emergency Number Association (EENA). In the modern and fast-moving world, governments need to plan for public safety events (disasters, terrorism, pandemics, etc.) and react to these events in a fast and efficient manner. In order to effectively carry out location analytics to help ensure public safety, three key elements are required:

  • Location data sourcing
  • Location data treatment and storage
  • Location visualization and analysis

Location data sourcing

One of the biggest challenges for public safety analysts is getting access to accurate and timely location data for analysis. Typical sources vary from cloud sources (web and mobile apps) to communication networks (mobile and WiFi). The reality is that all sources of location data need to be consolidated to provide the best picture for location analytics.

Location data from cloud sources

Location data from cloud sources are wide and varied.  Typically, location data gathered from mobile apps makes up a large portion and provides the widest picture of location intelligence. Mobile apps typically use GPS from the mobile phones to determine location, but other sources are utilized such as Wi-Fi nodes. As the main operating system providers in the market today (Google, Apple) are focusing on ensuring location data is not shared, it is envisaged that this source may become fractured in the coming years.

Location data from communications networks

Location data from communications networks is generated by mobile networks, Wi-Fi providers and Internet Service Providers. Such location data is more consistent and reliable as the data is generated from the communication network itself and does not rely on open sources. Location data is generated typically by the user’s proximity to network nodes (mobile/Wi-Fi cells etc.). The challenge here is the requirement to work with a variety of companies and attempt to coordinate and rationalize the location data received. This approach also relies on government legislation for the companies to generate and release the data.

The impact of 5G and IoT

While 5G and IoT will allow for greater location accuracy, the challenge of getting the data will increase exponentially due to the sheer volume of data and data sources. However, with the right expertise and technologies in place, the benefits of location intelligence for public safety can be multiplied with the advent of 5G and IoT.

Location data treatment and storage

Once the location data is obtained, it needs to be treated and stored for analysis. Location data treatment entails two main tasks:

  • Anonymization (when required): This has become a priority with the advent of rules like GDPR. Any identification of an individual’s location will need to be removed.
  • Location calculation and verification: All sources of data will need to be complied and turned in reliable/accurate location information. Artificial intelligence (AI) offers some great capability to both calculate accurate locations and identify trends in people movement which would take humans much longer to carry out. Indoor location calculation remains one of the greatest challenges for location analytics.

With the sheer number of data points available for location analytics, selecting the right storage solution to allow easy and timely access for analysis is key. Depending on the length of time required (3-5+ years) and the population of that country, the data stored could become truly enormous.  There are solutions available to handle petabytes of data and beyond.

Cloud storage solutions are starting to become acceptable mechanisms for storing vast quantities of data reliably and securely.

Location visualization and analysis

Once the location data is stored, it is crucial to have the right tools that will enable the concerned authorities to analyze the data and gain the intelligence required to effectively plan and respond to events. Geographic Information System (GIS) and mapping technologies combined with Location Intelligence provides the ideal mechanism to analyze where people are located during a specific event and also over a certain period of time.

Location intelligence for planning purposes

For planning purposes, it is key to public safety that the right location data is obtained to ensure the required support and services are available in order to respond to emergencies and most importantly prevent disasters. For example:

  • By being able to visualize where people spend their time during busy periods, agents/services can be deployed in the right locations at the right time ready to act in case of emergencies.
  • Trends can be examined over time as people migrate to different areas ensuring adequate continuous planning and adjustments.
  • Historical analysis can be carried out to understand how people reacted in an emergency situation and lessons learnt from their movements to ensure the required infrastructure and services are in place for future events.
  • During a pandemic, you can analyze whether people are adhering to lockdown or curfew rules

Location intelligence to better respond to events

When responding to an event, location analytics can be utilized in relative real-time to help public safety agencies to focus their resources in the right place and message the public appropriately. For example:

  • During a terrorist event, analytics can be used to understand if people are moving towards the danger area and utilize public warning systems to message them to stay away or take refuge in an appropriate location.
  • During a pandemic, analytics can help authorities to react quickly when there is an outbreak and take adequate action to ensure it is contained.

Location data provides valuable insights that are critical for the work of public safety organizations. By gaining historical and real-time location intelligence, they can anticipate trends and plan accordingly as well as react effectively to live events.

Article originally published by TM Forum, January 2022

Author: Stuart Walsh, Field Product Director, Creativity SoftwareStuart is a consultative technical leader with over 25+ years’ experience helping customers deliver solutions for success. Leader of the product division at Creativity Software. Passionate about introducing next generation solutions/services to Network Operators and Enterprises.