Senior Data Engineer

Looking for Freelance Opportunity In Data engineering?

We are seeking the Data Engineer Panelist @ 10+ years of Experience

This Is a Freelace_WFH/Remote_USA Shift Opportunity

Must have atleast 8+ Years of exp at any one One cloud Tech @ GCP, AZure or AWS

should have excellent knowledge @ Hadoop, python/pyspark, Bigdata,SQL & ETL

Role Overview:

We are seeking a highly experienced Data Engineer to serve as a panelist for evaluating candidates in technical interviews. This role requires a deep understanding of advanced data engineering technologies and practices, along with strong assessment capabilities.

Key Responsibilities:

• Conduct technical interviews to evaluate candidates expertise in Data Engineering.
• Provide detailed feedback on candidates performance and technical proficiency.
• Collaborate with the hiring team to refine interview processes and evaluation criteria.

Qualifications:

Mandatory Technical Expertise:

• Cloud Platforms: Advanced knowledge in at least one cloud platform (GCP, Azure, AWS), including services like BigQuery, Azure Synapse, or AWS Redshift.
• Big Data Frameworks: Expertise in Hadoop ecosystem components (HDFS, YARN, Hive, HBase), and Spark (including PySpark).
• Programming: Proficiency in Python (including libraries like Pandas, NumPy, and PyArrow) and Scala.
• ETL Processes: Extensive experience in designing and implementing complex ETL pipelines using tools like Apache NiFi, Airflow, or Informatica.
• Data Modeling: Strong skills in data modeling for both structured and unstructured data, including dimensional modeling and schema design.
• SQL Expertise: Advanced knowledge of SQL, query optimization, and handling large-scale relational databases (e.g., PostgreSQL, MySQL, or Snowflake).
• Data Storage & Processing: Familiarity with NoSQL databases (e.g., Cassandra, MongoDB) and distributed data processing techniques.
• DevOps for Data Engineering: Experience with CI/CD pipelines, containerization (Docker, Kubernetes), and Infrastructure as Code (e.g., Terraform).
• Security: Knowledge of data security protocols, encryption methods, and compliance standards like GDPR or HIPAA.

Additional Skills:

• Experience with real-time data processing frameworks like Kafka, Flink, or Storm.
• Hands-on knowledge of machine learning pipelines and MLOps tools like MLflow.
• Familiarity with data governance and cataloging tools like Apache Atlas or Collibra.

Soft Skills:

• Exceptional communication and interpersonal skills to effectively engage with candidates.
• Proven ability to assess technical knowledge accurately and provide constructive feedback.