Senior data engineers manage data-collecting systems and collaborate with colleagues. Here’s all you need to know about becoming a senior data engineer, their salary, what they do, and how to become one.
Senior Data Engineer
Senior data engineers are responsible for developing and maintaining data platforms, management tools, and pipelines. In this, the young data engineers are watched over both the design and the conduct.
Senior data engineers typically report to the director of data engineering or director of analytics at a corporation and are part of a data science or data analytics team. In order to provide efficient management, the Senior data engineer must be able to clearly communicate orders and information to the younger team.
The senior data engineer supervises and manages junior data engineering teams. You will also be expected to write reports and create presentations for senior business leadership teams as a senior data engineer. The senior data engineer must possess great communication skills in order for these reports and presentations to be understood and accepted. They must be clear, succinct, unambiguous, captivating, and convincing.
Requirement of a Senior Data Engineer
- Create, administer, and oversee projects for data storage and gathering systems.
- Data models and solutions should be suggested for the current data systems and implemented.
- Oversee the work of a junior data engineer.
- Check to see if the system was designed with data security and compliance requirements in mind.
Routine Tasks of a Senior Data Engineer
- Investigate data management problems while supporting varied teams.
- Work with architects and data analysts to outline the design specifications.
- Make progress reports for professionals who are not technically savvy.
- Test your data for accuracy to ensure accurate data processing.
Senior Data Engineer Job Description and Qualifications
- Our analytics and data warehousing system, which contains practically all of the organizational and political data, should be maintained and expanded.
- To support data and analytics projects, data engineering systems and pipelines must be secure, scalable, and dependable. Also, this entails sending data to affiliates and subsidiaries as well as integrating fresh data sources into our primary data warehouse.
- Utilize data from the data warehouse and other sources to create data visualizations and reports.
- Make scalable, repeatable technical programs and solutions that can be used to automate time-consuming data administration tasks.
- Evaluate, investigate, and irregularly muck around with different political and organizational data.
- Implement and maintain best-in-class security measures in our data warehouse and analytics environment while keeping an eye on the evolving threat landscape.
- Help other DAIR staff if necessary with the SQL, Python, or R code.
- Carry out additional tasks as directed; • Share these skills with other DAIR personnel
Qualifications
- Strong SQL and relational database administration abilities. ETL techniques are used to extract, transform, and load data into a relational database.
- The capacity to design, build, and deploy automated process chains using Python or R, particularly for data analysis and manipulation.
- A BA or BS in a similar field, or comparable work experience.
- The ability to read data, analyze and clean it, transform and recode it, combine multiple data sets, reformat data into extensive and lengthy formats, etc.
- Showed aptitude for picking up new abilities and troubleshooting code without assistance, as seen by looking up solutions to common programming problems on Google. Be able to pick up skills while working, in other words.
- Experience dealing with cloud infrastructure providers like Google Cloud and Amazon Web Services is preferred but not necessary.
- A track record of being able to prioritize and organize a variety of jobs and projects, as well as having excellent time management abilities.
- Experience with digital organizing tools like Action Network, ActionKit, or Blue State Digital, as well as an understanding of LANs or VANs, are all advantages but not necessities.
What Do a Senior Data Engineer Do
The department’s initiative to integrate data will be managed by the senior data engineer, who will also create a plan for the project and also manage a data warehouse infrastructure, and write scripts for data integration and analytics.
In order to establish requirements, mine and analyze data, integrate data from diverse sources, and build exceptional data pipelines to benefit the analytics needs of the organization and its affiliates, this role will work closely and cooperatively with members of the Data & Analytics and Development teams. They will also manage other proprietary systems and supervise the creation of an automated reporting system.
The Data, Analytics, and Infrastructure Resource (DAIR) is in charge of developing the Federation’s programmatic tools, web development, data systems, and analytics capabilities to provide the labor movement with long-lasting power. Numerous departments, state and local labor organizations, and other clients in the labor movement are supported by this team. Additionally, the department wants to give its partners the tools they need to carry out political and legislative mobilization and organize digital operations, more successfully and efficiently by investing in centralized infrastructure, training, and direct service work.
Senior Data Engineer Salary
Senior Data Engineers’ total yearly remuneration in the US is predicted to be $169,943, with an average salary of $128,022. In addition, these numbers represent the median or middle of the ranges using our proprietary Total Pay Estimate algorithm, which is based on user-submitted wage information. The anticipated annual compensation rise is $41,921. Additional pay may take the form of cash bonuses, commissions, tips, or profit sharing. All of the salary data that is currently available for this role falls between the 25th and the 75th percentile, with the numbers in the “Most Likely Range” falling in that range.
Below is a list of the top 10 American companies that employ senior data engineers, along with their total income, and these numbers represent the median or middle of the ranges. Among the employers are Coupang, Meta, and Hulu.
- Capital One $158,279 / yr
- Amazon $213,088 / yr
- Optum $162,211 / yr
- Aetna $156,373 / yr
- The Hartford $161,728 / yr
- Discover $146,085 / yr
- Meta $241,689 / yr
- Cognizant Technology Solutions $123,785 / yr
- Netflix Senior Data Engineer salary $211,868/ yr
- Wells FargoSenior Data Engineer salary $168,841 / yr
How To Become a Senior Data Engineer
It is a well-known truth that businesses’ propensity to process enormous amounts of data has contributed to the adoption of cloud solutions growing quickly in recent years. Additionally, data engineers contribute to the development of infrastructure and algorithms. Also, you must continually broaden your knowledge of programming languages, data management tools, data warehouses, and artificial intelligence/machine learning if you want to advance to the position of senior data engineer.
To construct a successful infrastructure, you must be an expert in the best tools and programming languages.
#1. Python
Python is the standard programming language for data engineering. It codes ETL frameworks, API connections, automation, and data munging. Python has also improved recently. Python’s latest developments. They include.
- Feather, an accessible binary file format, and IBIS, a toolset for transferring data from the Python environment to external storage systems like Hadoop or SQL.
- Panda for data analysis and manipulation; Matplotlib for the development of interactive, animated, and static visualizations; and NumPy for the management of data arrays.
- Learn about web application development frameworks like Flask and Django for creating backend development.
- Learn about Theano and TensorFlow, the deep learning libraries that offer high-quality pre-written codes. Theano aids in performance optimization, error detection, and diagnosis, whereas TensorFlow aids in the creation and training of machine learning models.
Amazon CodeGuru, dynamic programming, Python scripting for DevOps, advanced portfolio development and analysis, and more help improve your code. Keep abreast of new information as it emerges.
#2. Essential Data Engineering Tools
Access to raw data for the team is a data engineer’s responsibility, both for technical and non-technical members. Learn how to use the newest tools so you can stay competitive.
Cadence
Learn cadence to make coding simpler. Developing distributed apps, mastering Java and Python, and programming MySQL and Postgres storage are all beneficial.
Prefect
Building data pipelines and automating data are helpful. Your capacity to create, arrange, and manage pipelines of data as well as tasks and processes has increased.
SQL
Due to its usage of data transformation techniques and queries, SQL is a crucial tool for data access, updating, manipulation, and modification. Here are a few recent advancements in SQL:
- Create a temporary table using common table expressions (CTEs).
- Recursive CTEs and hierarchical data inquiry
- Using case when and data pivoting to write complex conditional statements
- Self-joins are SQL operations that link one table to another table.
- Track growth, among other things, by computing running totals.
Mongo DB
MongoDB would typically be the preferred option due to its unique attributes, including a distributed key-value store, document-oriented NoSQL capabilities, and MapReduce processing abilities, all of which are essential for data engineers who work with a lot of unprocessed, raw data.
Amazon Athena
This interactive query tool allows users to execute ad-hoc SQL queries on both structured and unstructured data. It speeds up the analysis of massive datasets while outperforming challenging ETL procedures.
Snowflake
In order to provide more insightful outcomes, Snowflake accelerates data engineering tasks by incorporating, modifying, and displaying data.
Spark and Apache
With Apache Spark, terabytes of streams might be processed in small batches. Additionally, it uses in-memory caching and provides faster query execution.
3. The Basics of Machine Learning
It gets more and more important to learn new topics as your profession develops and you need to extend your perspective. Regression, clustering, ensemble learning, advanced learning, processing natural languages, classification, multivariate reality, neural network training, and transfer learning are a few of the core computer techniques that are helpful to understand.
#4. Visualization of Data
You must properly explain your reports to superiors and corporate leaders, so it is imperative that you learn more about data visualization technologies. You can enhance your data visualization skills by adding more tools to your toolbox, such as Power BI, Qlik, Dundas BI, Adaptive Insight, Domo, Cluvio, Data Wrapper, Plotly, Tableau, etc. The use of Python-based data visualization tools like Matplotlib or Folium is currently widespread.
#5. Kubernetes and Docker
Airflow, Cassandra, Argo, and Kubernetes are some of the data processing solutions for containers that are becoming more and more well-liked. The main benefits of adopting containers for data processing are hardware independence, cloud computing, data independence, and framework. Currently, the most popular qualification for employment in data engineering is the ability to work with containers like Docker and Kubernetes.
#6. Be a T-shaped Professional
The letter “T”‘s horizontal bar represents overarching concepts that you need to be aware of. Consider fusing cloud computing and data warehousing. The letter “T”‘s vertical bar represents the need for strength in at least one particular area. You might, for instance, be an expert on Spark. Your communication abilities have improved as a result of your increased comprehension of several ideas and concepts.
A professional with excellent management talents also possesses X-shaped competencies in addition to T-shaped competencies.
#7. Consider Earning a Data Science Certification.
A focused, quick, online, and reasonably cost method of rounding out your skill set as a data engineer with experience in the field of data science is to obtain certification. Depending on the skill set you wish to build and emphasize on your CV, you can choose the credentials.
It might be a certification in complex information engineering, machine learning, artificial intelligence, or another area where the goal is to teach you how to use technology.
You must master these skills if you want to grow in your profession, specialize, and get employment. Learn more about the tasks and obligations of senior big data engineers, their compensation, typical interview topics, and potential career prospects.
What Are the Responsibilities of a Data Engineer?
The primary duties of a Senior Big Data Engineer are listed below for your convenience.
- Create, construct, and maintain these systems using Hadoop/Spark, Python, C/C++, and other distributed data analytics tools.
- Help with planning, building, setting up, and describing data management components.
- Recognize areas where the platform’s dependability, responsiveness, and quality can be improved.
- Satisfy the client’s expectations for functionality, availability, and performance.
- Work together with business analysts and data scientists
- Keep up your efforts and perseverance.
- Rapid introduction of new features
- Open pipes to allow for all projects.
What Is the Difference Between a Lead and a Senior Data Engineer?
A Lead Data Engineer’s major responsibility is to supervise a group of Data Engineers as they build and maintain data pipelines and guarantee data quality. In the hierarchy, they are placed above Senior Data Engineers, who are placed over (Junior) Data Engineers.
What Is Above Senior Data Engineer?
In the hierarchy, Lead Data Engineers are placed above Senior Data Engineers, who are placed above (Junior) Data Engineers.
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