Senior Angular Developer
San Francisco, California / $130000 - $170000
$130000 - $170000
San Francisco, California
SENIOR ANGULAR DEVELOPER
San Francisco, CA - HYBRID
$130,000 - $170,000 + BONUS
Looking for an experienced Senior Angular Developer to join an advanced team at a BioTech company that is a leader in its industry who is helping make a difference in science and healthcare.
This is a leading company that is focused on advancing research and healthcare, and is constantly growing and scaling their development.
As a Senior Angular Developer, you will take charge and establish the benchmark for developing the User Interface of a medical instrument project using Angular.
- Design and build out architecture for UI
- Expertise in Angular 2+
- Build out a single-page application
- Lead others from a design and technical leadership point of view
- Automated testing
YOUR SKILLS & EXPERIENCE
- Expert in Angular 2+
- Strong in Typescript
- Experience working with REST API
- Nice to have: C#, .NET
- 130,000- 170,000 base salary + Bonus
- Medical, Dental, Vision Insurance
- Equity Incentives
HOW TO APPLY
Please register your interest by sending your resume to Benjamin Palmore via the apply link on this page.
Weekly News Digest: 15th – 19th November | Harnham Recruitment post
Why it is hard to build a Big Data team | Harnham Recruitment post
Increasingly, I speak to managers who are adopting big data tools and developing PoCs to prove how they can make use of them. Just last week I spoke to a data architect who mentioned that if he didn’t get exposure to big data tech sooner rather than later, his current RDBMS skills may become redundant within the next few years. While that is likely an exaggeration, it is certainly an interesting point. Companies that would have never previously had the capability to interpret ‘Big Data’ are now exploring a variety of NoSQL platforms. In particular, the massive performance benefits gained from Spark and real-time/streaming tools have opened up a whole new world beyond just MapReduce. I don’t claim to be a data engineer, but as a recruiter for this sector, what I do is spend all day, every day interacting with big data developers, architects and managers (as well as keeping a close eye on the latest Apache incubator projects). Due to this, I have seen some recurring themes that have become trends when companies look to create and build their big data teams that are coming to the fore.
The demand for Big Data professionals is very much a present day issue as the data companies have grand plans for is waiting for the right data developer to use the best tech to extract valuable insights from it.
The best candidates receive massive interest, often gain multiple offers from a range of companies. Your business is now no longer just competing with large corporations such as Facebook, Twitter or Yahoo. Startups and SMEs are also vying for the best candidates.
Candidates are seeing pay rises twice that of the normal rate, as illustrated in our salary guide.
The number of candidates with hands-on, production level Big Data experience is incredibly limited. We go to great lengths to find the candidates who can add real value to companies.
The growth and exciting future for the big data industry has led to increased interest in big data jobs, particularly for those from RDBMS or software. engineering backgrounds. This leaves the industry in a difficult predicament: high demand + low supply = massive competition. There are countless examples of companies that have failed to recruit a Big Data team after a year of looking.
Competition to get ahead and stand outPlanning – Companies need to have a data road map detailing their future plans. Candidates want to clearly know what they are getting into and what to expect from a job.
Innovation – Why get stuck on batch processing? The most exciting positions that candidates love are in data innovations teams, playing with real-time/streaming tech and new languages.
Personal development, growth and training – with the data science market experiencing similar growth, many big data engineers are looking for a job that not only offers the chance to work with machine learning and similar fields; but training, mentoring towards clear career progression as standard.
Speed – the length of the interview process is often seen as a reflection of the amount of red tape developers have to go through to get a job. The longer and more convoluted the process, the more put off some people may be.
Complacency – don’t rest on your laurels, it’s unlikely that you’ll get 10s of CVs through when you are looking to fill a data role, so when you find a candidate you like, move swiftly to show your interest to them as quality candidates don’t come around often.
By implementing these small but effective improvements to your recruiting process and how you develop data talent will see you create a team that is a success in this ever more digital analytics landscape. Companies who don’t create and nurture strong, dynamic teams will fall by the wayside.
It’s Harnham’s job to help you achieve this goal. Get in touch with us to tell you how. T: (020) 8408 6070 E: firstname.lastname@example.org
Weekly News Digest: 11th – 15th July 2022 | Harnham Recruitment post
This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.
Tech To Freedom: Five habits of insanely productive software engineers
Software Engineering is a very special expertise, not to mention that it boasts some of the highest salaries around. Of course, as with all roles, years of experience can make a software engineer more efficient, but Tech for Freedom identify five tips for boosting your productivity, even if you’re just starting out. Here are just a couple: Learning by doing: Technology is evolving very quickly, so for a software engineer there is no time to rest on their laurels, they must be constantly learning. The speed of industry developments means that professionals don’t tend to have time to read hundreds of articles or take numerous courses in order to learn something new, instead they are likely to jump into the deep end and learn by doing.
Asking for help
It would be impossible for any one person to know everything. So, one of the essential survival skills for software developers is knowing how to ask for help.You may have ten years’ worth of Python programming under your belt, but now you need to develop something using a special module/tool/framework that you have never used before. The most efficient way to solve your problem is to employ help from someone who does have the experience in that tool. A good engineer knows that titles like ‘junior’ and ‘senior’ do not hold much weight, every engineer, no matter what title they have, has a unique knowledge and experience.Read further insights here.
Wealth Professional: Financial firms can't agree on how to address climate risk
While the risk to financial firms from climate change is considered a top priority, Bloomberg’s poll of 100 executives from financial services firm shows that there is still some way to go to address it.The survey revealed that while 85 per cent of firms have begun to assess the impact of climate risk, there is no consensus on how it should be embedded into risk management frameworks. Of these, 37 per cent are still in the early stages of planning how to incorporate climate risk into models and governance.When asked about the results, Zane Van Dusen, Head of Risk & Investment analytics products at Bloomberg, said: "…even those who say they have a robust model will be making significant changes over the next few years as our understanding and consensus around climate risk grows… More and better data will go a long way toward improving firms' ability to manage climate risk."Find out the key sticking points for the respondents here
The New Statesman: How data can help revive our high streets in the age of online shopping
High streets and town centres across the UK have undergone substantial transformations in recent years. Falling footfall, lost revenues and mounting fixed business costs have had a negative impact on traditional ‘bricks and mortar’ retailers, triggering a large wave of insolvencies across the UK.At the University of Liverpool, researchers have been utilising data and advanced geospatial algorithms to provide various retail-related research outputs and data products. The work is essential for the systematic monitoring of the performance of UK retail centres, giving the team a better understanding about retail centre exposure to current societal and market driving forces. Which will then allow them to track and predict the evolutionary trajectories of any given high street. As a result, in Liverpool they have estimated two types of retail catchments: drive times and walking distances, and then created profiles of those catchments based on numerous measures including deprivation, exposure to internet sales and geodemographics. It is hoped that these tools will aid policymakers, at both a local and national level, in making the decisions that will help revive flagging high streets and level up communities across the UK.Read more here.
Technology Works: AI Reliably Predicts Structure of RNA Molecules
The three-dimensional structure of biomolecules is crucial to their function. Therefore, researchers are interested in knowing more about their spatial structure, and with the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence.But for RNA molecules (ribonucleic acid) this technology is still very underdeveloped. Researchers at Ruhr-Universität Bochum have found a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence.“Identifying these self-similarities in an RNA sequence is like a mathematical puzzle”, explained researcher Vivian Brandenburg. The biophysical model for this puzzle cannot consider the cellular environment of the RNA – in other words it cannot process everything around the RNA.This is where AI comes into the mix. The AI can learn subtle patterns from the cellular environment based on known structures. It could then incorporate these findings into its structural predictions. But for this learning process, the AI needs sufficient training data – and this is lacking.To solve the problem of missing training data, the team used a trick. By working with known RNA structural motifs, researchers used a ‘reverse gear’ to allow them to generate almost any number of nucleotide sequences from the energy models of these structures, that would fold into these spatial structures. With the help of this ‘inverse folding’ the researchers generated sequences and structures with which they could train the AI.Find out if the process worked here.We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities.
To learn more about our work in this space, get in touch with us at email@example.com.
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