London / £60000 - £70000
£60000 - £70000
Data Manager Role
London- Hammersmith & Fulham- Office 4 Days per Week
An exciting tech company within the property industry has recently implemented a cloud-based data infrastructure and the foundation elements of data governance and data quality management.
They are looking for a Data Manager to help them to deliver their data vision and help embed a data-driven culture across the business.
You can expect to be involved in:
- Working closely with stakeholders across the business to lead the data management agenda daily. E.g. working closely with data owners and data stewards to ensure they understand and deliver to their data accountabilities.
- Providing guidance/oversight to all data impacting system changes ensuring consequences are understood.
- Management responsibility for one internal Data Quality Analyst.
- Undertaking data quality analysis and root cause issue identification and resolution as required.
- Working closely with the Analytics Engineer and Tableau Developer
YOUR SKILLS AND EXPERIENCE:
The successful candidate will have the following skills and experience:
- Experience in data profiling, data quality analysis, data cleansing, master data management and metadata management
- Experience working with a modern tech stack, ideally a cloud environment
- Expert SQL knowledge
- Experience implementing/operating data governance framework - understanding the challenges of effective data governance
- Experience working with 3rd party partners as data partners
- A salary of up to £70,000.
HOW TO APPLY:
- Please register your interest to this Data Manager role by applying via this website. For more information on this role or other roles in the Business Intelligence market, reach out to Kathryn Self at Harnham.
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.
The Six Steps Of Data Governance | Harnham Recruitment post
The value that data analysis can provide to organisations is becoming increasingly clear. But with all the buzz around the endless ways that data can be used to revolutionise your business processes, it can be overwhelming to know where to start. Fundamentally, what you can do with your data and how useful it may be will hinge on its quality. This is the case no matter what data you may have, whether that be customer demographics or manufacturing inventories. High-quality data is also imperative for utilising exciting and innovative new technology such as Machine Learning and AI. It’s all very well investing in tech to harness your data assets to, for example, better inform decision making, but you won’t be able to glean any useful analysis if the data is full of gaps and inconsistencies. Many will be looking at this new tech and be tempted to run before they can walk. But building quality data sets and water-tight, long-lasting processes will form the foundation for any future developments and should not be overlooked. This is where Data Governance comes into its own.Data Governance (DG) is an effective step in improving your data and turning it into an invaluable asset. It has numerous definitions but according to Data Governance Institute (DGI), “Data Governance is the exercise of decision-making and authority for data-related matters.“Essentially DG is the process of managing data during its life cycle. It ensures the availability, useability, integrity and security of your data, based on internal data standards and policies that control data usage. Good data governance is critical to success and is becoming increasingly more so as organisations face new data privacy regulations and rely on data analytics to help optimise operations and drive business decision-making. As Ted Friedman from Gartner said: ‘Data is useful. High-quality, well-understood, auditable data is priceless.’Without DG, data inconsistencies in different systems across an organisation might not get resolved. This could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence (BI) reporting and analytics applications.Data Governance programs can differ significantly, depending on their focus but they tend to follow a similar framework:Step 1: Define goals and understand the benefits The first step of developing a strategy should be to ensure that you have a comprehensive understanding of the process and what you would like the outcome to be.A strong Data Governance strategy relies on ‘buy in’ from everyone in the business. By stressing the importance of complying with the guidelines which you will later set, you will be helping to encourage broad participation and ensure that there is a concerted and collaborated effort to maintain high standards of data quality. Leaders must be able to comprehend the benefits themselves before communicating them to their team so it may be worth investing in training around the topic.Step 2: Analyse and assess the current dataThe next step is essentially sizing up the job at hand, to see where improvements might need to be made. Data should be assessed against multiple dimensions, such as the accuracy of key attributes, the completeness of all required attributes and timeliness of data. It may also be valuable to spend time analysing the root causes of inferior data quality.Sources of poor data quality can be broadly categorised into data entry, data processing, data integration, data conversion, and stale data (over time) but there may be other elements at play to be aware of.Step 3: Set out a roadmapYour data governance strategy will need a structure in which to function, which will also be key to measuring the progress and success of the program. Set clear, measurable, and specific goals – as the saying goes – you cannot control what you cannot measure. Plans should include timeframes, resources and any costs involved, as well as identifying the owners or custodians of data assets, the governance team, steering committee, and data stewards who will all be responsible for different elements. Including business leaders or owners in this step will ensure that programs remain business-centric.Step 4: Develop and plan the data governance programBuilding around the timeline outlined you can then drill down to the nitty-gritty. DG programs vary but usually include:Data mapping and classification – sorting data into systems and classifying them based on criteria.Business glossary – establishing a common set of definitions of business terms and concepts – helping to build a common vocabulary to ensure consistency.Data catalogue – collecting metadata and using it to create an indexed inventory of available data assets.Standardisation – developing polices, data standards and rules for data use to regulate proceduresStep 5: Implement the data governance programCommunicating the plan to your team may not be a one-step process and may require a long-term training schedule and regular check-ins. The important thing to realise is that DG is not a quick fix, it will take time to be implemented and fully embraced. It also may need tweaks as it goes along and as business objectives change. All DG strategies should start small and slowly build up over time – Rome wasn’t built in a day after all. Step 6: Close the loopArguably the most important part of the process is being able to track your progress and checking in at periodic intervals to ensure that the data is consistent with the business goals and meets the data rules specified. Communicating the status to all stakeholders regularly will also help to ensure that a data quality discipline is maintained throughout.Looking for your next big role in Data & Analytics or need to source exceptional talent? Take a look at our latest Data Governance jobs or get in touch with one of our expert consultants to find out more.
The German Market: Businesses Need To Adapt Or Miss Out On The Best Tech Talent | Harnham Recruitment post
After moderate market movement in the spring, the tech recruitment market in Germany is seeing more significant movement now, as businesses align their budgets and headcount for 2022. But there remains a real shortage of tech talent in all parts of the sector, from Data-Science and Software Engineering to Data Intelligence and Marketing Insights.Recent research conducted by the Berlin office highlights that hybrid and remote working options, as well as growth and upskilling potential, are the most important deciding factors in the German job market right now. The only distinct difference between those surveyed was in long term financial incentives – men preferred a workplace bonus, women regard a workplace pension and insurance benefit as a bigger priority when considering a job move. That aside, flexible working and maintaining a good work-life balance are set to stay. In this respect, Germany faces a particular challenge as culturally, onsite teams and face-to-face working relationships have always been of high importance to efficient operations. In addition, many players need to rely on a hybrid working model asking employees to come in at least some of the time which is additionally challenging due to the remote location of a lot of companies. Added to this, the country specific issues that Germany faces are likely to present ongoing challenges as we move into 2022. Germany has the broadest range of company type, size and structure in the world and the wide cultural and ethnic diversity creates a non-homogeneous market with micro-markets that need a bespoke approach when it comes to tech recruitment.Big Businesses slow to react The speed at which German businesses can react to environmental change is affected by high employee participation in Trade Unions and works councils (Betriebsräte). Change can be slow, even under normal circumstances, regardless of how much or fast leadership want to act. Listed businesses find it difficult to turn the ship around quickly. The logistical challenges combined with the need for larger organisations to shift their cultural mindset and tech environments are significant barriers to change.At the other extreme, however, SMEs that are much more agile and flexible are seeing this time as a real opportunity to attract the best tech talent, many of whom were more interested in the stability of roles in larger organisations. But times have changed, people want more control over their working conditions and greater transparency regarding outlook and overall company strategy when it comes to the data journey. More than ninety per cent of German businesses are SMEs (the highest ratio in the world) which makes the recruitment market exciting right now. It continues to be a candidate led market. The pandemic effect on BusinessEmployers were affected differently during the pandemic. Tech service providers, e-commerce businesses and retailers that already had online sales operations saw business go through the roof as consumer behaviours changed and shopping migrated online. Digital Marketing and Data Insights roles were in demand as retail businesses scaled up in response. This huge growth combined with the shortage of candidates as those in secure jobs sat tight. Those that did move, became quicker in their decision-making. Where we were used to seeing a steadily moving market, candidates taking their time deciding whether a role might right for them, things sped up. Work-life balance, location and job security were all major factors in the market, so those smaller, more agile clients that were quick to offer these things became very attractive to candidates who might have otherwise taken their time.Businesses that are less invested in their tech infrastructure or failed to upscale the backend were hit particularly hard. Some innovative start and scale-ups providing solutions at the point of sale such as hard- and software, went into hibernation. Where previously data architects and data engineers were not regarded as critical to business growth due to a focus on adding features and growing the userbase, are now quickly becoming integral to operations. Now the exponential growth phase has plateaued, the last 6 months has seen businesses investing in data initiatives to transform their operations. Those strategic businesses with the foresight to address this were able to weather the storm, those that did not faced real pressure, some even went into liquidation. The tech start-up space has been largely dormant as venture capital and private equity was hard to come by. We expect to see that pivot both in response to the pandemic spawning entrepreneurs and as gaps in the market for digital solutions are realised. Future-ProofingHaving taken stock, and with lessons learned, those businesses that have survived the pandemic are future-proofing, investing in data initiatives around more robust infrastructures. Data Engineers, Software Engineers, DevOps and platform teams are high in demand and the recruitment market is running hot. The more classic customer-focused roles are also being advertised – Data Scientists, Social Media Analysts, Multi-Channel Marketing, Data Insights.New Roles in TechAs mentioned by my Nordic colleague Amanda Snellman there is an interesting evolution in tech. Brand led businesses are looking to their marketing teams to find ways to maintain a competitive advantage in the market are actively seeking talent to bridge the gap between Data and Marketing where candidates can speak the language of both disciplines. This is one of the more positive outcomes of the pandemic – silos are being broken down and operations are moving towards multi-disciplined product teams that are charged with budgets and responsibilities. These hybrid roles (Data Managers, Product Managers, Product Owners and similar) are falling out of the need for candidates who can understand the analysis, see the potential data can have in responding to consumer needs and who are able to transform those insights into actionable measures that can move businesses forward in a meaningful way. Data Scientists and Analysts who have a real understanding of what data can do to solve consumer problems and help a business grow. The Ripple EffectThe ripple effect of the pandemic will be felt for years to come. Currently, businesses are reacting out of necessity. The pandemic has resulted in many data initiates being prioritised. Those tech projects which may have taken several years to reach the top of the business agenda are now a huge focus. Communication is easier, and online meetings facilitate decision-making. But with home and work lines becoming more blurred and employees being looped in 24/7 the next pandemic may be burnout. Is remote working here to stay?Absolutely yes, despite the downsides. There is a slow realisation that if there is an internet connection, and a candidate can work, they can be based anywhere. Big businesses need to get on board with that to secure the best talent. There has always been remote working in tech and German businesses have long looked to other countries to fulfil their tech recruitment needs. Change was already happening; the pandemic has just exaggerated the curve. How can businesses make themselves more attractive in 2022?Going into 2022, choice will be key. Candidates have been in short supply for some time and as the German market approaches year-end this remains unchanged. As always, we continue to be selective in who we send to interview, which our clients appreciate, and most we put forward get to interview. Once at this stage, if hiring managers be open-minded to candidates’ requirements and respond accordingly then there will be measurable success in recruitment. The candidate led market is here to stay for some months yet.Looking to build out your data team? Get in touch with one of our expert consultants. Looking for your next opportunity? Check out our Data jobs in Germany.
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