Data Analytics/Operations Engineers

Hire Data Analytics/Operations Engineers

Data is increasingly being used by businesses across all sectors to assist them make crucial business choices such as which new goods to produce, which new markets to join, which new investments to make, and which new (or current) customers to target. They also utilize data to discover inefficiencies and other problems that need to be addressed in the firm.

The data analyst’s duty in these organizations is to provide numerical values to these essential business processes so that performance can be reviewed and compared over time. A data analyst’s work is more than simply crunching numbers; they must also understand how to utilize data to help a firm make better choices.

What are the opportunities in Data Analytics/Operations Engineering?

There’s no doubting that companies are going digital, and there’s lots of room for expansion. What is fueling this expansion? Without a doubt, its data has become the new oil. Big data has recently assisted firms in making better-informed choices and providing best-in-class products and services, which have been widely acknowledged. If you’re not convinced how serious organizations are about big data analytics, consider the following figures: From 2019 to 2027, the worldwide big data analytics market, which was worth USD 37.34 billion in 2018, is predicted to increase at a CAGR of 12.3%, reaching USD 105.08 billion. It’s easy to see how big data analytics will thrive in the future, particularly with such high stakes.

Operations engineers, on the other hand, may find employment in a wide range of sectors. They may work in the construction business, where their primary tasks revolve on equipment. Operations engineers may work as cost estimators, consultants, and distribution managers since their skills are transferable across sectors. They may be used by local governments, organizations, and huge corporations alike.

Do you want to learn more about Data Analytics and Operations Development? Then this is the article for you.

What are the duties and obligations of a Data Analytics/Operations engineer?

A data analyst’s job is to collect and arrange data in order to make useful conclusions from it. “The duties of data analysts vary based on the sort of data they’re working with (sales, social media, inventory, etc.) as well as the unique client project,” explains Stephanie Pham, an analyst at Porter Novelli. Data analysts work in a variety of fields, including healthcare, retail, and fast-food restaurants. Employers that wish to learn more about their consumers or end users might benefit from data analysts’ insights. Analysts might be involved at any stage of the inquiry. If you work remotely as a data analyst, you may be required to instruct others on how to utilize your data collection system.

The answer to the question “What does a data analyst do?” may vary depending on the sort of firm and the degree to which data-driven decision-making processes have been implemented. However, the following are a data analyst’s general responsibilities:

  • The development and upkeep of data systems and databases, as well as the repair of coding mistakes and other data-related problems.
  • Data is collected from primary and secondary sources and organized in a way that people and robots can comprehend.
  • Data sets are analyzed statistically, with an emphasis on trends and patterns that may be used for diagnostic and predictive analytics.
  • demonstrating the importance of their work in relation to local, national, and worldwide trends affecting their business and industry.
  • Creating executive reports that use pertinent data to effectively express trends, patterns, and predictions.
  • Identifying potential for process improvements, promoting system upgrades, and establishing data governance rules in collaboration with programmers, engineers, and organizational leaders.
  • The responsibility of an operations engineer is to guarantee that a company’s operations, such as manufacturing and shipping, function efficiently and fulfill factory and management criteria.
  • These engineers are in responsible of any repairs or improvements to equipment, as well as consulting with other department heads to fine-tune their operational systems.

How Do I Get a Job as a Data Analytics/Operations Engineer?

Data analytics jobs are accessible in a variety of industries, and there are many ways to enter into this in-demand sector. Here are some methods to finding a job as a data analyst, whether you’re just starting out in the professional world or changing fields. Here’s how to get started if you want to pursue a career in this in-demand sector.

  • If you’re new to data analysis, you should start with the basics. Gaining a thorough grasp of data analytics will help you determine if this is the right career for you while also providing you with marketable abilities.
  • Typically, obtaining a remote data analytics job requires a set of specialized technology abilities. These are some core talents you’ll most likely need to be employed, whether you’re studying via a degree program, a professional credential, or on your own. Examine several job postings for roles you wish to apply for, and concentrate your research on the programming languages or visualization tools necessary.
  • The most effective method to learn how to extract value from data is to work with it in real-world circumstances. Look for degree programs or seminars that include real-life projects and data sets. You may also create your own projects by combining publicly accessible public data sets.

    Now, let’s go through the abilities you’ll need to acquire in order to become a competent Data Analytics/Operations engineer:

Qualifications for becoming a Data Analytics/Operations engineer

Deductive reasoning, analytical thinking, problem solving, and good written and verbal communication skills are required for Data Analytics/Operations engineers. The first stage is to develop the core skills that will help you acquire a high-paying Data analyst employment. Let’s have a look at some of the things to be mindful of:

  1. database management software

    Any data analyst’s toolset should include Microsoft Excel and SQL. While Excel is commonly used in many sectors, SQL can handle bigger data volumes and is often seen as a need for data analysis.
  2. Computer programming languages

    You’ll be able to handle vast volumes of data and solve tough equations if you master a statistical programming language like Python or R. Despite the fact that Python and R are two of the most popular programming languages, it’s a good idea to review many job descriptions for a position you’re interested in to evaluate which language will be most beneficial to your organization’s development.
  3. Visualization of data

    To be a great data analyst, you must be able to clearly and convincingly present your results. Knowing how to show data in charts and graphs may assist teammates, managers, and stakeholders understand what you’re working on. Visualizations are created using programs such as Tableau, Jupyter Notebook, and Excel. Understanding the fundamentals of what data tools accomplish can greatly improve your business. A solid grasp of statistics and arithmetic can help you choose the tools to employ to tackle a given issue, identify data flaws, and interpret the findings. They should also be able to provide engineering assistance for the design, development, operation, and maintenance of equipment, processes, or facilities.

Where can I find remote Data Analytics/Operations Engineer jobs?

One of the most in-demand careers is Data Analytics/Operations Engineer. Getting a remote job is a detail-oriented job that requires education, abilities, and experience. Regular practice, a thorough grasp of programming languages, and a daily routine are required to work as a remote Data Analytics/Operations engineer.

Works offers the finest remote Data Analytics/Operations engineer positions available to assist you in achieving your professional goals. We provide you with the resources you need to learn, develop, and succeed in your Data Analytics/Operations Engineer career. Join a worldwide community of like-minded developers and find full-time remote programming jobs to further your career.

Job Description

Responsibilities at work

  • Encourage the use of a DataOps approach to the delivery process in order to automate data provisioning, testing, and monitoring.
  • Increase deployment frequency to minimize development cycles and address data analytics demands as soon as possible.
  • Collaborate with data architects to create and optimize a data pipeline for data solutions, including data science products.
  • Make certain that the data pipelines are scalable and highly performant.
  • Make sure you incorporate DataOps and automation projects for production-ready solutions.
  • Using the DABL framework, include agile working practices into the delivery process.
  • Work with cross-functional teams to develop a common DataOps framework for continuous delivery and quality assurance.
  • To accurately assess the effect of services, stay up to date on the newest technological advances, new prospects, and industry trends.


  • Engineering or computer science bachelor’s/degree master’s (or equivalent experience)
  • 3+ years of data and analytics expertise, with knowledge of developing data technology platforms and management (rare exceptions for highly skilled developers)
  • Experience developing and coding in one or more programming languages, such as Python, Java, Scala, C++, C#, and others.
  • Knowledge of microservices and REST API endpoints.
  • Solid understanding of relational databases, especially SQL.
  • Working knowledge of ML deployment and operations.
  • Knowledge of queuing systems and distributed systems.
  • Solid understanding of best practices in software development.
  • Previous expertise designing and managing high-performance scalable systems and multi-threaded applications is required.
  • Solid grasp of business intelligence and data warehouse methodologies.
  • Expertise in tools such as Apache Spark, the ELK stack, Apache Airflow, the Hadoop Ecosystem, Apache Kafka, and Amazon Redshift.
  • To communicate successfully, you must be fluent in English.
  • Work full-time (40 hours a week) with a 4-hour overlap with US time zones.

Preferred skills

  • Understanding of ML methods, optimization algorithms, and data mining.
  • Understanding of large-scale distributed systems such as Hadoop and Spark.
  • Knowledge with GCP and AWS is advantageous.
  • Ability to convey complex technological concepts to technical and non-technical stakeholders.
  • Outstanding understanding of the Agile development process.
  • Excellent technical, analytical, and problem-solving abilities.