Senior Site Reliability Engineer
Santa Clara, California / $175000 - $200000
$175000 - $200000
Santa Clara, California
SENIOR SITE RELIABILITY ENGINEER
SANTA CLARA, CA
$175,000 - $220,000
If you're a technologist by nature with a passion for distributed systems and large-scale infrastructure, this is the opportunity for you. You will have the opportunity to make a business-wide impact and develop greenfield projects at a unicorn start-up.
Working with the latest technologies, this data-driven tech company is working to bring analytics to all sectors of business. Acknowledged as one of the 2021 Top Places to Work in the Bay Area, this company prides itself on its collaborative, employee-focused culture. This Senior Site Reliability Engineer will be a great addition to this team.
As a Senior Site Reliability Engineer, you will make an impact by owning the production of customer-facing applications. The day-to-day responsibilities will include developing software in Go, Python, or Java, as well as maintaining containerization at scale. This Senior Engineer will be responsible for discovering, monitoring, and deploying all vulnerabilities for high-availability systems and should be excited by this challenge.
YOUR SKILLS AND EXPERIENCE:
- Extensive previous experience maintaining large-scale, high-traffic e-commerce applications
- Continuous hands-on development of Python, Go, and Java
- Expertise with distributed systems or content distribution networks
- Experience with cloud platforms (GCP ideally)
- Kubernetes, Docker, or Terraform experience
- $175,000 - $220,000
- Headquarters in Santa Clara, CA
- Full Medical, Dental, Vision coverage
- 401k and retirement saving plans with company match
- Paid parental leave
- Annual wellness, tech, and family planning stipends
- Educational reimbursement
HOW TO APPLY:
Please register your interest by sending your CV to Natalie Endres via the Apply link on this page.
Data Engineer Or Software Engineer: What Does Your Business Need? | Harnham US Recruitment post
We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly. So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!… or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals. Data Engineers When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL. Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders. An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another. Software Engineers – DataSimilar to a Data Engineers, Software Engineers – Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently. Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient. This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model. Which is right for your business? If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker. If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It’s more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse. It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled.That’s not to say a Data Engineer can’t work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual’s past experience and the projects they have worked on, rather than looking at their previous job titles. If you’re looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help. Take a look at our latest opportunities or get in touch if you have any questions.
Site Reliability Engineering: The Next Big Career Wave To Ride | Harnham Recruitment post
The adoption of new technologies, combined with the increased speed in application delivery and pressure for automation, has caused a spike in demand for IT operations professionals with comprehensive and up to date skills and knowledge. As a result, careers that offer improvements to system reliability and efficiency, such as DevOps and Site Reliability Engineering (SRE), are experiencing a flood of interest. At Harnham, we are seeing this play out before our eyes – so what is SRE and how can professionals break into this escalating space?
Where did SRE come from?
Much of the excitement around SRE originated from Google putting it on the map as the next big role to recruit for. Today, Google defines it simply as ‘what happens when you ask a software engineer to solve an operational problem.’Since then, it has gained substantial momentum and in January 2022, LinkedIn listed SRE as the 21st job with the highest global demand throughout the past five years.
SRE is often considered a step up from DevOps engineering or from cloud engineering, building on existing infrastructure to reach system reliability. Whilst DevOps is an overarching concept aimed at ensuring the rapid release of stable, secure software. SRE involves prescriptive ways of achieving reliability and has been developed with a narrow focus in mind: to create a set of practices that allow for improved collaboration and service delivery.
DevOps Engineers are ops-focused engineers who solve development pipeline problems, while Site Reliability Engineers are development-focused engineers who solve operational, scale and reliability problems, while working closely with software development and IT operations teams. Once the system is “reliable enough”, SRE efforts shift to adding new features or creating new products.
What route can those already in the market take to secure SRE roles?
For companies looking to hire into the SRE space, candidates with previous experience in the role will naturally take precedence. But those who are open to hiring outside of the SRE sphere, will likely prioritise applicants from a software or systems engineering background above those with DevOps engineer or a data engineer title.
For Software Engineers looking to transition, a strong starting point would be to improve their skills in troubleshooting, incident management and monitoring, maintaining infrastructure in the cloud environment and experience with the Linux operating system. Systems Engineers will likely already have knowledge on Linux and troubleshooting topics. So boosting their skills in coding and programming languages like C, Java, and Python and ensuring that they're able to write code as well as review it, is highly recommended.
How can candidates give themselves the best chance of securing a SRE role?In previous years software engineers would be working in a team of other engineers and communicating with largely technical stakeholders. But now the role is expected to fulfill tasks that were traditionally reserved for business intelligence professionals, such as collaborating with both technical to non-technical colleagues.
As a result, when choosing between candidates, one of the fundamental deciding factors for hiring managers, outside of technical ability, are the soft skills that complement their expertise. Applicants who can demonstrate experience in, or a tenacity for, cross department collaboration and an ability to interact with colleagues with varying levels of expertise, will hold the competitive edge.
So how should companies and the sector be improving the flow of talent into SRE roles?
SRE is growing exponentially, and we expect it to continue to do so. Findings from the 2022 Upskilling Report found that 40 per cent of respondents felt that a SRE operational framework is a must-have. The most limiting factor to the continuation to this growth will be whether the pipeline of talent is able to sustain the rate of expansion. There is a particular bottleneck when it comes to junior talent. Companies may be eager to employ senior candidates with extensive experience and are willing to pay exceptionally high salaries to secure them, but they often overlook the prospect of hiring into more junior positions or establishing internship programmes to help cultivate and develop theses talent streams. SRE as a career may not have been the radar of many students until relatively recently but as awareness increases, the demand for courses to reflect this is likely to rise.
When we consider the evolution of other emerging roles such as Data Engineering, we can see how they went from being a niche specialism to commanding a whole university master's courses dedicated to the subject. SRE is likely to go the same way. To bypass expensive salary wars, organisations should also consider if there is any scope for reskilling or upskilling existing staff. Larger companies in particular could benefit from selecting a few people from their software teams and upskilling them to be SRE engineers, which will streamline and cut the costs of their processes. Upskilling as a Site Reliability Engineer could be a great alternative avenue for those not considering going down a management path but who still want to pursue career progression. Looking for your next big role in Data & Analytics or need to source exceptional talent? Take a look at our latest SRE jobs or get in touch with one of our expert consultants to find out more.
Keepers of the Data Kingdom: the Analytics Engineer | Harnham US Recruitment post
If it seems the Data world is drilling down further into niche specialities, you’re right. Considering the swathes of information sent and received on a day-by-day, minute-by-minute, and second-by-second basis, is it any wonder? The sheer volume, depending on your business and what you want to know, requires not just a Data team, but must now include someone with a particular skillset, including the tech-savvy analyst who can speak to the executive team.So, who holds it all together? These swathes of information. Who organizes the information in a cohesive order, so anyone with a map, can make their own analyses? Enter the Analytics Engineer.What Makes an Analytics Engineer an Analytics Engineer?Though it’s a rather new speciality within the Data Scientist scope—think Machine Learning Engineer, Software Engineer, Business Analyst, etc—at its core, the definition of an Analytics Engineer is this: “The analytics engineer sits at the intersection of the skill sets of data scientists, analysts, and data engineers. They bring a formal and rigorous software engineering practice to the efforts of analysts and data scientists, and they bring an analytical and business-outcomes mindset to the efforts of data engineering.” Michael Kaminsky, consultant, and former Director of Analytics at Harry’s.In other words, analytics engineers, using best software engineering practices transform data through testing and documentation so that data analysts begin with cleaner data to analyze. As technically savvy as the engineer must be, they must also be able to explain to stakeholders what they’re looking at so they can formulate their own insights. Five Roles and Responsibilities of the Analytics EngineerLike all new niche specialities, there are core responsibilities to consider as well as that of skillsets required to either study to become an Analytics Engineer or to discover if you’re one already. How? Consider the questions you ask, your studies within Data Science, Computer Science, Statistics, and Math, and your balance between technical skills and soft skills. Below are five things to consider when thinking about this role:Programming language experience. Experience with programming languages like R and Python along with strong SQL skills which are at the core of this role. DBT technology knowledge. As the driving force behind the rise of Analytics Engineer as a separate role, it’s imperative anyone interested in pursuing it should have a firm grasp of DBT — the Data Build Tool — that allows the implementation of analytics code using SQL. Data tracking expertise using Git. Data modelling. Clean, tested, and raw data which allow executives and analysts to view their Data, understand it within the database or its warehouse. Data transformation. Analytics Engineers determine what Data is most useful and transform it to ensure it fits related tasks. It’s part of building the foundational layer so businesses can answer their own questions. Key Changes Leading to the Shift in Data RolesWith every technological advancement their comes new players to the field. The difference is here is that the job description already existed. We were only missing a title. But from the traditional Data team to the modern Data team, there are a few key changes that point directly to the rise of this niche field. Cloud warehouses (like Snowflake, Redshift, BigQuery) and the arrival of the DBT the foundational layer which can be built on top of modern data warehouses are the first two that come to mind. Then, the Software-as-a-Service (SaaS) tools like Stitch and Hevo are capable of integrating Data from a variety of sources, and the introduction of tools like Mode and Looker allows anyone interested in drawing insight from Data to do so on their own.Who Needs an Analytics Engineer? Small or Large Businesses?The short answer is it depends. But the general rule follows that while both large and small companies can benefit from having this professional on their staff, there are different things to consider. For example, a small business may be able to find what they need in a single individual. The Analytics Engineer is something of a jack-of-all-trades. Larger businesses, on the other hand, may already have a Data team in place. In this case, an Analytics Engineer adds to your team, something like an additional set of eyes increasing insight drawn from those large swathes of Data we spoke about earlier.So, what’s next for the role of Analytics Engineer? Who knows? The roles of any Data industry professional is constantly evolving. If you’re interested in Analytics Engineering, Machine Learning, Data Science, or Business Intelligence just to name a few, Harnham may have a role for you. Check out our latest Data & Analytics Engineering jobs or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 – 4999 or send an email to firstname.lastname@example.org. For our Arizona Team, contact us at (602) 562 7011 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.
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