Data Analyst

Impronics Technologies; Gurugram District; On-site; INR 800,000 - 1,200,000; Full time

Job summary

Impronics Technologies is looking for a skilled Data Engineer based in Gurugram, Haryana. In this role, you will design, implement, and maintain robust data pipelines and architectures vital for our data-driven initiatives.

Your responsibilities will include collaborating with data scientists and business teams to ensure efficient data flow and integration, all while ensuring data integrity and security across systems. A bachelor’s degree in Computer Science or related field is required, along with at least 4 years of relevant experience.

Qualifications

• Minimum of 4+ years of experience in a data engineering role or similar.
• Proficiency in programming languages like Python, Java, or Scala.
• Strong knowledge of SQL and NoSQL databases.

Responsibilities

• Design, build, and maintain scalable data pipelines.
• Collaborate with cross-functional teams for data integration.
• Develop and optimize data models and schemas.

Skills

Data pipeline development

Data integration

Data modeling

ETL process management

Performance optimization

Data quality assurance

Collaboration

Documentation

Education

Bachelor’s degree in Computer Science, IT, or Engineering

Tools

Python

Java

Scala

SQL

NoSQL databases

AWS

Azure

Google Cloud

Apache Airflow

Docker

Job description

We are seeking a skilled Data Engineer to join our team. In this role, you will design, implement, and maintain robust data pipelines and architectures to support our data-driven initiatives. You will work closely with data scientists, analysts, and business teams to ensure the efficient flow and integration of data across various systems, allowing for insightful analysis and decision-making.

Key Responsibilities:

• Data Pipeline Development: Design, build, and maintain scalable and efficient data pipelines to collect, clean, process, and store data from multiple sources.
• Data Integration: Collaborate with cross-functional teams to integrate data from various databases, APIs, and third-party services into centralized data platforms.
• Data Modeling & Architecture: Develop and optimize data models, schemas, and databases to support data analysis and reporting.
• ETL Process Management: Implement and manage Extract, Transform, Load (ETL) processes to ensure data is properly prepared and available for analytics.
• Performance Optimization: Monitor and optimize the performance of data processing and storage solutions to handle large volumes of data efficiently.
• Collaboration: Work closely with data scientists, analysts, and business teams to understand data needs and provide data solutions that meet organizational goals.
• Data Quality Assurance: Ensure data integrity, quality, and security across all data systems and pipelines.
• Documentation: Maintain clear documentation of data engineering processes, architectures, and workflows.

Qualifications:

• Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. A Master’s degree is a plus.
• Experience: Minimum of 4+ years of experience in a data engineering role or similar, including building and maintaining data pipelines and ETL processes, and familiarity with cloud platforms (AWS, Azure, Google Cloud) and big data tools (Hadoop, Spark).
• Technical Skills: Proficiency in programming languages like Python, Java, or Scala; strong knowledge of SQL and NoSQL databases (MySQL, PostgreSQL, MongoDB); experience with data warehousing solutions (Redshift, BigQuery, Snowflake); familiarity with data orchestration tools (Apache Airflow, Luigi); and knowledge of data modeling, governance, and security best practices.
• Soft Skills: Strong analytical and problem-solving abilities; excellent communication skills, both written and verbal; and ability to work collaboratively in a fast-paced, cross-functional team environment.

Preferred Qualifications:

• Familiarity with data visualization tools (Tableau, Power BI).
• Experience with machine learning frameworks and workflows.
• Knowledge of containerization tools (Docker, Kubernetes).
• Understanding of DevOps principles for data infrastructure management.