Target Hiring Data Engineer Job In Karnataka।Apply Right Now

Job Title : Data Engineer

Salary : Rs 10,60000 per year

Company : Target

Location : Karnataka

Qualifications : Bachelor’s degree

Experience : 4 year

ABOUT TARGET

Target Corporation, headquartered in Minneapolis, Minnesota, is a outstanding American retail enterprise founded in 1902. Known for its tremendous community of stores across the US, Target has located itself as a leading store presenting a huge variety of merchandise, along with apparel, groceries, electronics, and home items.

Target differentiates itself via its “Expect More. Pay Less.” logo promise, aiming to provide first rate products at low-cost charges. The company operates each brick-and-mortar stores and an increasing e-trade platform, allowing clients flexibility in buying alternatives.

In latest years, Target has centered on enhancing its virtual skills to meet evolving consumer options, consisting of identical-day delivery offerings and contactless payment options. The business enterprise has also prioritised sustainability initiatives, aiming to reduce its environmental footprint thru various applications and partnerships.

Job Overview

A Data Engineer designs, constructs, and keeps scalable records pipelines and infrastructure to transform uncooked information into beneficial formats for evaluation and intake. They collaborate with records scientists and analysts to understand records wishes, make sure information awesome, and optimize performance. Key responsibilities consist of database layout, ETL (Extract, Transform, Load) strategies, and enforcing facts warehouses or lakes. Strong programming abilities in languages like Python, Java, or SQL are essential, collectively with proficiency in Big Data technologies consisting of Hadoop, Spark, or Kafka. Data Engineers additionally play a crucial characteristic in enforcing facts protection and privacy measures to shield sensitive statistics.

Requirements and skills for a Data Engineer:

1. Programming Languages: Proficiency in programming languages is important. Python and Java are usually used for information engineering obligations because of their versatility and sturdy libraries for statistics manipulation and processing. SQL competencies is vital for database querying and control.
2. Database Management: Strong information of database structures is crucial, which incorporates each conventional relational databases (e.G., PostgreSQL, MySQL) and NoSQL databases (e.G., MongoDB, Cassandra). Data Engineers need to understand schema design, indexing strategies, and question optimization strategies.
3. Big Data Tools and Frameworks: Experience with Big Data technology such as Hadoop (HDFS, MapReduce), Apache Spark, Apache Kafka, and Apache HBase is frequently required. These gear permit dealing with large-scale information processing, real-time analytics, and distributed computing.
4. Data Warehousing: Understanding of facts warehousing concepts and era (e.G., Amazon Redshift, Google BigQuery, Snowflake) is essential. Data Engineers design and hold information warehouses to help reporting, analytics, and choice-making techniques.
5. ETL (Extract, Transform, Load): Proficiency in designing ETL pipelines to extract records from numerous assets, redesign it in step with industrial corporation necessities, and cargo it into records warehouses or information lakes. Tools like Apache Airflow, Apache NiFi, or custom scripts are used for ETL methods.
6. Data Modeling and Architecture: Knowledge of facts modeling techniques (e.G., superstar schema, snowflake schema) and revel in in designing scalable and green records architectures. This includes data facts flows, dependencies, and optimizing records garage and retrieval.
7. Data Quality and Governance: Ensuring statistics high-quality and reliability thru statistics validation, cleaning, and errors handling techniques. Implementing records governance frameworks to keep statistics integrity, safety, and compliance with hints (e.G., GDPR, HIPAA).(Data Engineer) 
8. Version Control and Collaboration: Proficiency in version manage structures (e.G., Git) for managing codebase adjustments and collaboration with statistics scientists, analysts, and distinct stakeholders. Strong verbal exchange abilities to articulate technical ideas to non-technical audiences.
9. Cloud Platforms: Experience with cloud structures which includes AWS, Google Cloud Platform (GCP), or Azure. Data Engineers leverage cloud services for scalable garage, compute assets, and controlled services like AWS S3, EC2, Lambda, GCP BigQuery, and Azure Blob Storage.
10. Machine Learning Operations (MLOps): Understanding of MLOps ideas to install, show, and hold device studying fashions in manufacturing. Data Engineers collaborate with statistics scientists to mix models into facts pipelines and ensure their scalability and normal overall performance.(Data Engineer) 
11. Analytical Skills: Ability to analyze complicated datasets, understand inclinations, styles, and anomalies, and derive actionable insights. Experience in building dashboards or reviews for stakeholders the usage of BI device (e.G., Tableau, Power BI) is exceptional.
12. Problem-Solving and Troubleshooting: Strong problem-solving capabilities to discover and remedy statistics-related problems, average performance bottlenecks, and tool disasters proper away. Data Engineers have to proactively show display screen statistics pipelines and infrastructure to make certain clean operations.

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