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Data Management

Top 10 DataOps Jobs Aspirants Should Apply For In August

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Top 10 DataOps Jobs Aspirants Should Apply For In August

DataOps jobs are in high demand from DataOps engineers to be well placed in the data-centric market

Data science offers a wide range of data engineering jobs and DataOps jobs across the world in the data-centric market. DataOps vacancies are opening in all tech and data-related companies that need effective database management in 2022 and beyond. DataOps jobs include multiple roles including a DataOps engineer to build data products and engineer the production department with database management. The global DataOps platform market size is expected to hit US$57.90 billion in 2030 at a CAGR of 24.2%. Data engineering jobs also include the DataOps engineer profession with lucrative salary packages per annum. DataOps vacancies can be searched on professional platforms such as LinkedIn and many more while receiving more data engineering jobs. Let’s explore some of the top ten DataOps jobs or DataOps vacancies to apply for in August 2022 to work for the effective database management system.

Top ten DataOps jobs in August 2022
DataOps engineer expert at Solvay

Location: Belgium/China/France/Portugal

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Responsibilities: The engineer can take on the data-intensive portion of the data life cycle with a specific focus on automating jobs associated with cleaning, preparing, and wrangling the data within the data pipelines. It is essential to improve the availability, accessibility, and integration of data while implementing, maintaining, and supporting the platform data management environment(s) from ingestion to storage, encoding, preparation, and delivery to the respective users.

Qualifications: The candidate must have an Engineering degree with three years of experience as the specified role or five years of experience in DevOps implementation with a specialization in Ops than data and CI/CD.

Click here to apply

DataOps engineer at GeoPhy

Location: The Hague

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Responsibilities: The engineer should help the development of infrastructure and framework for data quality assurance while contributing to decisions about the technology stack, particularly as it relates to end-to-end data flow and automation. It is essential to support the orchestration of data products by improving metadata solutions.

Qualifications: The candidate must have at least four years of experience in data engineering and software development with knowledge of SQL, Python, Scala, Spark, data modeling, Apache Airflow, and web applications.

Click here to apply

DataOps engineer at Adyen

Location: Amsterdam

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Responsibilities: The engineer should create, maintain and iterate on the current data infrastructure, anticipate growth and look for structural changes required in the big-data offering at Adyen, as well as work closely with users of the data infrastructure and arrive at pragmatic solutions.

Qualifications: The candidate must have a solid background in designing, scaling, and maintaining data infrastructure solutions and sufficient knowledge of the Hadoop ecosystem (Hive, Yarn, HDFS) with Spark, and securing clusters with Ranger and Kerberos.

Click here to apply

DataOps engineer at Lenskart

Location: Bengaluru

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Responsibilities: The engineer should ensure the performance, security, and availability of databases, and handle common database procedures, such as upgrade, backup, recovery, migration, etc. while profiling server resource usage, optimizing, and tweaking as necessary.

Qualifications: The candidate must have a strong knowledge of MySQL 5. x database management, Linux shell scripting, SQL and NoSQL databases, MongoDB, InnoDB, and many more.

Click here to apply

DataOps engineer at Jefferson Frank

Location: Oslo

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Responsibilities: The engineer should help bring models into production and ensure they are running efficiently and securely, take care of the deployment of ML models in a live environment, as well as maintain an efficient and secure environment running in the Cloud.

Qualifications: The candidate must have an understanding of data science, data analytics, bash scripting, Terraform, Azure, networking, and security, as well as container technology.

Click here to apply

DataOps engineer at bubble

Location: Wokingham

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Responsibilities: The role is for high visibility with end-to-end functionality with a greater level of verbal and written communication.

Qualifications: The candidate must have eight to ten years of experience in Azure DevOps, Data Bricks, ADF, Azure Integration, Docker, Kubernetes, OpenShift, ArgoCD, GitOps, Jenkins CI, and Ansible.

Click here to apply

DataOps engineer at E.ON Energie Deutschland GmbH

Location: Germany

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Responsibilities: The engineer should continuously develop the modern data platform in the cloud with a focus on operation and the development and orchestration of ETL processes in cooperation with the data engineers. The duty also includes the provision and further development of monitoring, alerting and logging solutions for infrastructure, SaaS solutions and data pipelines.

Qualifications: The candidate must have a degree in computer science with sufficient experience in the Azure cloud components, CI/CD, Airflow, Python, SQL, Docker, Kubernetes, and other IT operations. It is necessary to know German at B2 level.

Click here to apply

DataOps engineer at FanDuel

Location: Remote in the UK

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Responsibilities: The engineer should help in provisioning and maintaining infrastructure, building automated tools that will organize, model, and present data as a coherent product and offer it to stakeholders, as well as implementing data lake, warehouse, and lakehouse architectures leveraging the knowledge of data archiving and retrieval solutions.

Qualifications: The candidate must have a deep understanding of data lake permissions, AWS, GCP, data ingestion, SNS, SQS, Kafka, Spark, and IaC technology.

Click here to apply

DataOps engineer at Colgate-Palmolive

Location: Partial remote, New Jersey  

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Responsibilities: The engineer should help establish DataOps platforms, processes, and frameworks to improve data quality and cycle times, and implement continuous integration and delivery of data pipelines while creating and implementing automated test frameworks for testing data pipelines.

Qualifications: The candidate must have a Bachelor’s degree in any technical field with strong computer science fundamentals, cloud data technologies, Java, Python, and SQL.

Click here to apply

DataOps engineer- data analytics platform at ING

Location: Poland

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Responsibilities: The role is to help in building cloud-ready state-of-the-art data analytics platform with data analytics platform components development and maintenance, SecOps activities, as well as Infra-as-Code.

Qualifications: The candidate must have sufficient experience in big data tools, Infra-as-Code, data ingestion pipelines, ML models, Alluxio, Trino, Superset, and Hive.

Click here to apply

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Data Management

Top 5 DataOps Tools To Use In 2022

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Top 5 DataOps Tools To Use In 2022

Generally, data-driven companies mainly struggle with data quality issues. Massive amounts of data, changing data storage requirements due to changing company regulations, and the various types of data sources and formats do make data management quite difficult for companies. Hence, the need for robust DataOps tools to automate and simplify data life-cycle phases is of utmost importance at this point. Here are, some of the most important DataOps tools that professionals can use in 2022.

Census: Census is the leading platform for operational analytics with reverse ETL (extract, transform, load), offering a single, trusted location to bring your warehouse data into your daily applications. It sits on top of your existing warehouse and connects the data from all of your go-to-market tools, allowing everyone in your company to act on good information without requiring any custom scripts or favors from IT.

Dbt: Dbt is a transformation workflow that allows organizations to deploy analytics code in a short time frame via software engineering best practices such as modularity, portability, CI/CD, and documentation. Dbt Core is an open-source command-line tool allowing anyone with a working knowledge of SQL to create high-quality data pipelines.

Databricks: Databricks Lakehouse Platform is a comprehensive data management platform that unifies data warehousing and AI use cases on a single platform via a web-based interface, command-line interface, and a software development kit. It includes five modules: Delta Lake, Data Engineering, Machine Learning, Data Science, and SQL Analytics.

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Mozart Data: Mozart Data is a simple out-of-the-box data stack that can help you consolidate, arrange, and get your data ready for analysis without requiring any technical expertise. It provides a web-based interface for data scientists to work with data in various formats, including CSV, JSON, and SQL. In addition, Mozart Data provides a flexible data modeling layer that allows data scientists to work with data in various ways.

Datafold: Datafold helps businesses prevent data catastrophes. It has the unique capacity to detect, evaluate, and investigate data quality concerns before they impact productivity. It combines machine learning with AI to provide analytics with real-time insights, allowing data scientists to make top-quality predictions from large amounts of data.

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Agri-Trade

How Spectral Data Can Transform Agri-Trade

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How Spectral Data Can Transform Agri-Trade

Agritech services based on spectral sciences to pave the way to advanced and transformed Agri-trade

With the rapid advancements in Agricultural technology, the sector is witnessing new waves of tech-fueled progress and innovations that have put Indian Agriculture on the Global Agritech map. Today, the Indian Agritech vista is reaping the benefits of digitization by embracing a plethora of new-age technologies like AI, ML, Big Data, IoT, etc.

The widespread adoption of deep technology is opening up a new frontier for agriculture in post-harvest systems. Let’s take wheat, for instance, the moisture content, dry gluten, starch, etc. are essential factors to assess the nature and endurance of the commodity. These factors determine the prices during market transactions. However, during manual evaluations of quality, the process of certifying the exact quality of the wheat crop is a hard and subjective method that results in additional surcharges for both buyers and sellers. Additionally, the conventional quality checking methods are time-consuming and cost inefficient.

This is where next-gen Agtech technologies like IR-Spectroscopy come into play. Infrared Spectroscopy enables agriculture researchers to learn and discover the various properties of a food commodity by illuminating the specimen with a given light which when reflected back reveals the essential characteristics of the said specimen. Spectral data can aid in the creation of a quality map for key commodities, allowing processors to drive procurement efficiency through geo-varietal and process optimisation. Discovered by the Great Indian Scientist and Nobel Laureate C.V Raman, this revolutionary technology has recently become prominent in the field of agriculture. The results have been phenomenal, to say the least. This technology is being leveraged across the post-harvest agriculture landscape for quality assessment processes which can reveal the purity and important statistics of any harvested crop.

Leveraging the latest technologies such as AI/ML can work wonders in such a situation. The system has helped effortlessly remove all contingencies of manual errors by eliminating the scope of subjectivity from the process. Existing NIR technology is combined with AI/ML to increase accuracy as the system gains experience. Some technologies used in AI/ML are deep learning, Edge Inferencing, Artificial Intelligence Accelerators, etc. This utilization of AI-based spectroscopic technology will prove instrumental in rapid quality testing, lowering the testing time to less than 1 minute, while warranting that all potential holdups during the quality reporting process cease to exist. By leveraging this technology, a slew of physical and chemical factors that impact trade decisions can be ascertained with ease. Overall, digital, rapid, and accurate quality assaying solutions make mapping quality across the value chain easier.

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A number of new-age Agtech startups are helping various stakeholders in the food value chain by saving costs in the procurement of raw materials through rapid quality assessment technology. Conventionally speaking, Agritech businesses have always depended on individual crop-wise manual expertise for precisely determining the quality of products. Spectroscopy can significantly resolve a number of issues in post-harvest agricultural processes by eliminating biases and standardizing the entire operation through technology by measuring various chemical quality parameters of numerous food commodities such as grains, animal feed, and milk. This technology can prove valuable in conducting fast-paced adulteration testing that can considerably ensure food safety standards and build trust in the end user.

Agritech businesses are leveraging a number of novel spectroscopic tools to determine the quality of food grains before they reach our pantries. In the post-harvest supply chain, IR spectroscopy is used for Gram, Wheat, Moong, Soybean, Paddy, Maize, Chili, Rice, Toor dal, Urad, Bajra, Mustard, Chana Dal, Turmeric, etc.

These are also utilized for rapid quality assessment of key chemical parameters of food commodities across Grains, Oilseeds, Pulses, Spices, and Beverages segments. Key commodities include Wheat and its derivatives, Rice, Paddy, Maize, Mustard, Turmeric, and Chilli Powder as well as animal feed commodities like Soybean Meal, Cottonseed Meal, Mustard Meal, Mustard Cake, De-Oiled Rice Bran, DDGS, RiceBran/Rice Polish,  ice Bran Meal and Huller Bran. There are also various Portable solutions that specialize in gauging the composition as well as conducting the adulteration testing of milk in less than 1 minute. The technology also offers a way for the rapid detection of adulterants like Urea, Salt, Detergent, Ammonium Sulphate, and Caustic Soda.

Agritech startup innovations are addressing some of the most pressing issues in agriculture and food products such as suboptimal food quality, wastage, lack of traceability etc. The Agritech frontier is evidently on the cusp of a revolution. The induction of deep tech has not only enhanced the output quality but has also built trust and transparency across multiple post-harvest food value chains. The post-harvest agtech industry has a great deal of potential to be redefined and reshaped by technologies like IR spectroscopy. The potential for blockchain and spectral data integration is enormous. It can provide 100% traceability and foster trust in the value chain. This technology is transforming the food and agriculture sectors by improving organizational decision-making capabilities.

Despite the fact that technology has advanced substantially, the future lies in the managed services for the stakeholders at procurement, warehousing, processing, and distributors. We need agritech firms that can offer comprehensive inspection and fulfillment services based on spectral sciences to pave the way to a more advanced and transformed Agri-trade.

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Author:

Taranjeet Singh Bhamra, CEO & Founder, AGNEXT

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Business Intelligence

The Relationship Between Business Intelligence And Data Management

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The Relationship Between Business Intelligence And Data Management

With the continuous growth of data flowing in the digital world today, BI tools are becoming more sophisticated

Business intelligence refers to the various applications, tools, and processes used to analyse data and provide information to assist organisations in making better business decisions. It can be used in a variety of ways, including increasing company income, enhancing operational efficiency, and increasing customer happiness. Companies can use BI to design systems depending on what data they can acquire and what they intend to achieve in the end, allowing them to gain a competitive advantage in an ever-growing industry. Irrespective of the company’s size, industry, or desired objective, the goal of using BI is to help firms make better decisions and boost their overall performance.

Data management is a subset of business intelligence that comprises a wide range of applications utilised throughout the BI system. Data management entails a cycle of gathering data, guaranteeing its accuracy, integrating different types, and managing the overall data management approach in line with the business goal. Effective data management procedures are critical to the success of any business intelligence solution.

Since its conceptual beginnings in the 1950s, business intelligence has grown significantly, and you must recognise that business intelligence is not only for major corporations. We are currently in an era where small structures, like start-ups, are much more prevalent, and most BI providers have recognised this shifting dynamic and are modifying their software to meet the needs of customers. This is especially true for SaaS, or Software-as-a-Service, providers.

Another difference is that it is no longer as complex a tool as it formerly was. Sure, it’s still a professional tool; data administration is difficult to work even with the best system on the planet. Having said that, with the introduction of the Cloud and SaaS in the early twenty-first century, BI has grown into something much more accessible than local software, which used to need installation on every system in the organisation; an installation that may represent a substantial expense. Business Intelligence tools and data management have gotten more practical and accessible to end-users as the SaaS and Cloud world has evolved.

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Data management has a significant advantage for BI. With the continuous growth of data flowing in the digital world today, BI tools are becoming more sophisticated and can more readily model data, providing firms with a huge competitive advantage.

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