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Data engineer Job Description: Top Duties and Qualifications

A Data Engineer, or Data system engineer, converts the raw data into usable information for the company. They are responsible for developing and maintaining data processing software like databases. For this, they create unique data infrastructure, run tests, and update the systems.

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Data Engineer Duties And Responsibilities

Data engineers create and maintain an optimal data infrastructure. They combine raw information to create machine-readable formats. These are the major duties and responsibilities of a data engineer:

  • Putting together large, intricate data sets to satisfy both functional and non-functional business needs.
  • Determining, creating, and implementing internal process improvements, such as redesigning infrastructure for increased scalability, improving data delivery, and automating manual procedures.
  • Building necessary infrastructure using AWS and SQL technologies. This will enable effective data extraction, transformation, and loading from a variety of data sources.
  • Reformulating existing frameworks to maximise their functioning.
  • Building analytical tools that make use of the data flow and offer a practical understanding of crucial company performance indicators like operational effectiveness and customer acquisition.
  • Helping stakeholders, including the data, design, product, and executive teams, with technical data difficulties.
  • Working on data-related technical challenges while collaborating with stakeholders, including the Executive, Product, Data, and Design teams, to support their data infrastructure needs.
  • Remaining up-to-date with developments in technology and industry norms can help you to produce higher-quality results.
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Data engineer Job Description Examples:

Example 1

Experience:10+ Years

What is the Job like?

  • Designing and building optimized data pipelines using cutting-edge technologies in a cloud environment to drive analytical insights.
  • Constructing infrastructure for efficient ETL processes from various sources and storage systems.
  • Collaborating closely with Product Managers and Business Managers to design technical solutions aligned with business requirements.
  • Leading the implementation of algorithms and prototypes to transform raw data into useful information.
  • Architecting, designing, and maintaining database pipeline architectures, ensuring readiness for AI/ML transformations.
  • Creating innovative data validation methods and data analysis tools.
  • Ensuring compliance with data governance and security policies.
  • Interpreting data trends and patterns to establish operational alerts.
  • Developing analytical tools, programs, and reporting mechanisms.
  • Conducting complex data analysis and presenting results effectively.
  • Preparing data for prescriptive and predictive modeling.
  • Continuously exploring opportunities to enhance data quality and reliability.
  • Applying strong programming and problem-solving skills to develop scalable solutions.

    Who should apply for this role?

  • 10+ years of hands-on experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed data pipelines.
  • High proficiency in Scala/Java and Spark for applied large-scale data processing.
  • Expertise with big data technologies, including Spark, Data Lake, Delta Lake, and Hive.
  • Solid understanding of batch and streaming data processing techniques.
  • Proficient knowledge of the Data Lifecycle Management process, including data collection, access, use, storage, transfer, and deletion.
  • Expert-level ability to write complex, optimized SQL queries across extensive data volumes.
  • Experience with RDBMS and OLAP databases like MySQL, Redshift.
  • Familiarity with Agile methodologies.
  • Obsession for service observability, instrumentation, monitoring, and alerting.
  • Knowledge or experience in architectural best practices for building data lakes.

    Good to Have:

  • Passion for testing strategy, problem-solving, and continuous learning.
  • Willingness to acquire new skills and knowledge.
  • Possess a product/engineering mindset to drive impactful data solutions.
  • Experience working in distributed environments with global teams.

    CTC: As per the Industry standards, negotiable.

    Immediate joiner preferred. Appreciate your time invested in us.

    Job Types: Full-time, Permanent

    Salary: From ₹1,200,000.00 per year

    Benefits:
  • Flexible schedule
  • Health insurance
  • Paid sick time
  • Provident Fund
    Schedule:
  • Day shift
  • Monday to Friday
  • Morning shift
  • US shift
    Supplemental pay types:
  • Performance bonus



    Ability to commute/relocate:
  • Bengaluru, Karnataka: Reliably commute or planning to relocate before starting work (Required)

    Application Question(s):
  • Can you join us immediately? What is your Notice period?

    Education:
  • Bachelor's (Preferred)

    Experience:
  • SQL: 8 years (Required)
  • Data Architecture: 5 years Location: In person

    • What Does A Data Engineer Do?

    Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Data engineers often work for businesses in a variety of industries. They assist them in managing and storing crucial data related to business operations. To create and implement these systems across departments, they collaborate closely with other data professionals and employees of the information technology department. Their responsibility is to code and maintain data systems using their understanding of various programming languages. They can also be in charge of implementing cybersecurity.

    Data Engineer Skills And Qualifications

    A successful data engineer should have flexibility, creativity, and the capacity to process constructive criticism. They should have excellent interpersonal communication skills in order to collaborate with all other teams:

    • Knowledge of how to create and improve data sets, “big data” data pipelines, and infrastructures.
    • Being able to do root cause analysis on data and procedures both internally and outside to find possibilities for improvement and provide clarification.
    • Outstanding analytical abilities related to coping with unstructured datasets.
    • Capacity to develop procedures that support task management, data structures, dependence, and metadata.
    • Knowledge of data models, data mining, and segmentation techniques.
    • Understanding of programming languages like Java and Python.
    • Familiarity with Hadoop, HBase, MapReduce, and other suitable platforms.
    • Excellent understanding of operating systems like UNIX, Linux, and Windows.
    • Strong project management and organisational skills.

    Data engineer Experience Requirements

    Most companies look for candidates with 5+ years of experience. Candidates should have knowledge and experience of using software and tools. Experience in big data tools like Kafka, Spark, and Hadoop; databases like Cassandra and pastgres; pipeline tools like Airflow, Luigi, and Azkaban; AWS close services like Redshift, RDS, EMR, and EC2; stream-processing systems like Spark-Streaming and Storm; and object-oriented scripting languages like Scala, C++, Java, and Python.

    Data engineer Education and Training Requirements

    A data engineer position usually requires a bachelor’s degree in data engineering, big data analytics, computer engineering, or a related field. A master’s degree in this field will also be advantageous.

    The Certified Professional-Data-Engineer designation from Google certifies a person as being knowledgeable about the fundamentals of data engineering and qualified to work as a professional or associate in the field.

    The IBM Certified Data Engineer – Big Data certification, concentrates on big data-specific applications rather than broad skills.

    The CCP Data Engineer for Cloudera certification shows that a person has proficiency with ETL analytics and tools.

    A wide number of topics are covered by secondary certificates, such as the Microsoft Certified Solutions Expert (MCSE), which has a specialised sub-certification called the MCSE: Data Management and Analytics.

    Data Engineer Salary Expectations

    The average yearly wage for a data engineer in India is ₹13,91,002. The salary range could range from ₹3.1 to 21.0 lakhs. The level of education and experience required for the position, as well as the employer’s location, all affect data engineer compensation. These salaries are based on the 1.7K wages that Indeed.com received.

    Job Description Samples for Similar Positions

    If this job description isn’t exactly what you’re looking for, consider these samples for similar positions:

    Data engineer Job description FAQs

    What Makes A Good Data Engineer?

    Data engineers are expected to have a wide array of technical expertise. A skilled data engineer designs, monitors, and updates data systems for businesses using their understanding of computer languages and business requirements. Additionally, they have good interpersonal skills, which enable them to communicate with staff members from various departments to address technical issues or database requirements.

    Who Does A Data Engineer Report To?

    A Data Engineer often reports to the Director of Data Engineering in large organisations or information technology companies to receive assignments, debug programming problems and upgrade existing databases. Data Engineers may serve as IT department employees in smaller businesses, working under the direction of the IT Manager.

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