Job Description

Key Responsibilities:

Design, develop, and maintain scalable

ETL (Extract, Transform, Load)

pipelines and data architectures. Integrate data from multiple sources into centralized

data warehouses

or

data lakes

. Optimize data storage, transformation, and retrieval processes for performance and scalability. Collaborate with cross-functional teams (Data Science, Analytics, BI, and Engineering) to deliver data solutions. Implement data governance, quality, and security best practices. Automate data workflows and monitor data pipeline performance and reliability. Develop and maintain

data models

, schemas, and metadata documentation. Work with cloud data services (AWS, Azure, or GCP) to manage and deploy data solutions. Troubleshoot data issues, perform root cause analysis, and ensure high data integrity. Continuously evaluate new technologies to improve data engineering processes.

Required Skills & Qualifications:

Bachelor's or Master's Degree

in Computer Science, Information Technology, or related field.

5-7 years

of hands-on experience in Data Engineering or a related role. Strong proficiency in

SQL

and experience with relational and non-relational databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra). Expertise in

data pipeline tools

such as

Apache Airflow, Kafka, Spark, NiFi, or Talend

. Strong programming skills in

Python, Java, or Scala

for data manipulation and automation. Experience with

big data frameworks

(Hadoop, Spark, Hive, HBase). Proven experience in

cloud platforms

(AWS Glue, Redshift, S3, Azure Data Factory, GCP BigQuery, etc.). Hands-on experience with

data modeling

,

ETL design

, and

data warehouse architecture

. Familiarity with

DevOps

,

CI/CD pipelines

, and

containerization (Docker, Kubernetes)

is a plus. * Strong analytical and problem-solving skills with attention to detail.