Data Engineer

Key Responsibilities:
Data Pipeline Development:
Build, test, and maintain scalable and reliable ETL/ELT pipelines to ingest, process, and transform data from various sources.
Database Design and Optimization:
Design and manage data models, schemas, and storage solutions (e.g., relational and non-relational databases) to ensure efficient querying and analytics.
Data Quality and Governance:
Implement robust data validation, monitoring, and governance processes to ensure data accuracy, consistency, and compliance with regulatory standards.
Cloud Infrastructure and Integration:
Deploy and optimize cloud-based data solutions, leveraging tools and services like AWS, GCP, or Azure.
Collaboration:
Work closely with data analysts, data scientists, and business stakeholders to understand data requirements and deliver effective solutions.
Performance Optimization:
Monitor and optimize system performance, query efficiency, and data delivery SLAs.

Required Skills and Qualifications:

1. Technical Expertise: Proficient in Python, SQL, and data engineering frameworks (e.g., Apache Spark, Kafka, Airflow). Hands-on experience with ETL/ELT tools like dbt, Talend, Informatica, or Apache NiFi. Strong knowledge of relational databases (e.g., PostgreSQL, MySQL) and data warehouses (e.g., Snowflake, BigQuery, Redshift).
5. Cloud Platforms: Experience with cloud data platforms like AWS (e.g., Glue, Redshift), GCP (e.g., BigQuery, DataFlow), or Azure (e.g., Synapse, Data Factory). Familiarity with cloud storage solutions like S3, GCS, or Azure Blob Storage.
8. Big Data Tools: Knowledge of distributed data processing frameworks like Apache Hadoop, Spark, or Flink. Experience with NoSQL databases (e.g., Cassandra, MongoDB).
11. Data Governance: Understanding of data privacy laws (e.g., GDPR, CCPA) and governance practices.
13. Soft Skills: Excellent problem-solving skills with the ability to work in a fast-paced environment. Strong communication skills for collaborating with cross-functional teams.

Preferred Skills:

Experience in CI/CD pipelines for data workflows.
Knowledge of MLOps and integrating data engineering solutions with machine learning pipelines.
Familiarity with monitoring tools like Grafana, Prometheus, or CloudWatch.
Certifications in AWS, GCP, or Azure.

Skills:

• Data Engineering
• English
• ETL
• Nifi
• Python
• Snowflake

.