Data Engineer at Dallas,Texas (in-person interview)

About the Role

Key Responsibilities

• Build and maintain data warehouse solutions on Snowflake.
• Develop and optimize data pipelines for batch and streaming data processing.
• Implement data ingestion, transformation, and orchestration processes using Snowflake and related tools.
• Manage real-time data processing workflows with Spark Streaming, Flink, Kafka Streams.
• Collaborate with data architects, analysts, and marketing teams to ensure data quality, security, and governance.

Requirements

• 10 - 12+ years of experience in data engineering, with at least 3-5 years focused on MarTech, CDP, and data warehousing.
• Bachelor's or master's degree in computer science, Information Systems, or related field.
• Hands-on experience with Snowflake cloud data platform, including data ingestion, transformation, and orchestration.
• Strong background in building and maintaining data warehouse solutions on Snowflake.
• Proficiency in SQL, Python, or other programming languages for data processing and automation.
• Experience with ETL/ELT tools, data pipeline development, and Apache Airflow workflow management.
• Proficiency in real-time data processing (Spark Streaming, Flink, Kafka Streams).
• Experience with cloud data warehouses Snowflake and data lakes (Delta Lake, Iceberg).
• Familiarity with NoSQL (MongoDB, Cassandra) and key-value stores (Redis, DynamoDB).
• Experience with batch & streaming pipelines (Kafka, Kinesis, Pub/Sub).
• Experience with Azure cloud platforms, Azure Event Hubs, and their integration with Snowflake.
• Understanding of marketing technologies, customer data platforms, and data integration challenges.
• Knowledge of data quality, data governance, and security practices in data engineering.
• Strong problem-solving skills and ability to optimize data processes for performance and scalability.
• Good communication and teamwork skills.

Nice to Have

• Relevant certifications (e.g., Snowflake, Azure Cloud, and Big Data).
• Familiarity with NoSQL databases like MongoDB and Cassandra.