Job Description:
A Data Engineer oversees designing, building, and keeping up the infrastructure for collecting, storing, and processing data. They set up data pipelines, keep databases organized, and make sure that data is safe and works together. Their work is very important for making data analysis and machine learning possible, making sure that data is of high quality, and helping organizations make decisions that are scalable, efficient, and based on facts. Data engineers are very important to the whole data ecosystem.
You will oversee planning, creating, and managing the data architecture that supports our insights and data-driven applications in this position. To make sure that data is gathered, saved, and processed effectively and consistently so that we can make wise decisions and create business value, you will collaborate closely with data scientists and analysts.
| Salary | 13-15 Lakhs Per Annum |
| Educational Requirement | ANY GRADUATION |
| Work Arrangement | WORK FROM OFFICE |
| Gender Preference | BOTH MALE AND FEMALE |
| Skills Requirement | Airflow, PySpark, Trino & Hive, SQL, python |
| Experience Requirement | 4–5 years |
| Location | Pune, Chennai |
About company
Technology-driven Mavlra Technologies Pvt Ltd provides clients in various industries with cutting-edge IT solutions. The company’s main goal is to help businesses accomplish digital transformation, increase productivity, and streamline operations by utilising cutting-edge technologies. Data analytics, cloud solutions, IT consulting, and software development are just a few of the services that Mavlra Technologies provides.
The company takes pride in its client-centric strategy, which emphasises customised solutions that address the particular requirements of each client. Mavlra Technologies works to uphold the highest levels of quality, integrity, and dependability in each project with a staff of knowledgeable experts. They stay at the forefront of the business because of their work culture, which encourages collaboration, ongoing learning, and technical advancement.
Roles and Responsibilities
1. Data Pipeline Development
- To coordinate the transfer and transformation of data between systems, create, build, and maintain robust data pipelines.
- Use Airflow to orchestrate workflows and ensure timely and reliable data processing schedules.
- To minimize downtime, continuously improve pipeline performance and fix problems.
2. Ingestion and Transformation of Data
- Make certain that data platforms are ingested with a range of flat file formats, including CSV, JSON, and mainframe-based files.
- Develop and execute data transformation logic for standardizing, improving, and cleaning raw information for further analysis.
- Ensure that all ingested data satisfies the organization’s format and quality requirements.
3. Spark-based processing
- Use PySpark for large-scale data processing to help manage high-volume datasets effectively.
- To handle the intricate needs of business logic and transformation, use unique User Defined Functions (UDFs).
- To achieve optimal performance and resource efficiency in distributed computing environments, optimize Spark jobs.
4. Development of SQL
- Create, maintain, and enhance SQL scripts used for analytics, reporting, data extraction, and manipulation.
- Assist business intelligence and analytics activities by ensuring that SQL queries are optimized for correctness and effectiveness.
- Work together with interested parties to understand reporting needs and offer useful information.
5. Integration of Snowflake Data
- By loading and integrating processed data layers into Snowflake, you can make sure that the data is secure, accessible, and organized.
- Create protocols for the Snowflake environment’s data integrity monitoring and maintenance.
- Work with analytics teams to facilitate the smooth access to curated data sets for reporting and analysis.
6. Data Quality and Metrics
- Run automated tests to make sure file size restrictions and data consistency are upheld across the data pipeline.
- By tracking and reporting daily data metrics, you can make sure that data dependability and integrity are maintained.
- To guarantee that the data is trustworthy for business choices, make sure that problems with data quality are found and fixed quickly.
7. Collecting requirements and working together
- interact with technical specialist business users to find out the information requirement and translate them into technical solution
- Promote efficient stakeholder communication to guarantee project delivery success and alignment with corporate goals.
- Provide technical know-how and direction to support ongoing process improvement and the creation of effective data solutions.
Required skills and Experience
- familiarity with SQL and Spark environments and performance tuning.
- familiarity with best practices for data governance and security.
- demonstrated capacity to handle maximum file size restrictions and process and manage huge files.
- knowledge of logging, monitoring, and reporting procedures for data measurements.
Key Skills
1. Airflow
- plans and automates procedures and data processing.
- allows for visual task management and tracking.
- widely used to orchestrate ETL (Extract, Transform, and load) jobs.
2. PySpark
- makes it possible for Python on Apache Spark to handle big datasets.
- enables rapid, distributed data analysis and processing.
- used a lot for large data and machine learning jobs.
3. Trino & Hive
- Both are sql based instructions in the analysis monitoring of hug data sets
- Trino can connect to multiple data sources and run fast, scattered queries.
- Hive stores data in Hadoop and enables SQL-like searches for data analysis.
4. SQL
- Language for managing and querying relational databases.
- Makes efficient data organizing, filtering, and extraction possible.
- Essential for data analysis and reporting.
5. Python
- a widely used programming language that is simple to use and comprehend.
- widely used in data analysis, automation, and software development.
- enables a wide range of libraries for applications such as data processing and visualisation.
FAQ
1. What kind of career path does a data engineer typically take?
To gain experience with data tools and technologies, a data engineer usually begins as a junior data engineer or associate, working on smaller-scale projects. After that, they can advance to mid-level positions where they can take on increasingly challenging tasks and responsibilities, such as senior data engineers or data engineers. They can rise to leadership roles like chief data officer, data architect, or lead data engineer with experience and skill.
2. Who are data engineers?
A data engineer is a expert who is designs and develops the system for the
collection ,processing and storage of data for business and analysis purposes. For data scientists and analysts, they build data pipelines and guarantee data reliability. For example, a data engineer at Spotify develops processes to manage streamed data in order to produce unique playlists.
3. What Does a Data Engineer Do?
Building and maintaining data pipelines and infrastructure is the responsibility of a data engineer, who also makes sure that data is dependable and available for analytics. They improve systems for performance and combine data sources.
A data engineer at bank checks the transaction data support identification of Froud
4. What Are the Requirements for Becoming a Data Engineer?
To work as a data engineer, you must be proficient in cloud platforms (AWS, Azure, GCP), programming languages (Python, SQL), and tools like Apache Spark or Airflow. Collaboration abilities and data modeling are also crucial. For instance, data engineers can automate intricate data procedures by becoming proficient with Airflow.
Share this content: