Job description:

• Education: A bachelor's or master's degree in computer science, software engineering, data engineering, or a related field is typically required. Some companies may also consider equivalent industry experience in lieu of a degree.
• Experience: Significant professional experience in data engineering or a related field is typically required. For a Staff-level role, you might need at least 7+ years of relevant work experience, although this can vary.
• Data Engineering Skills: Proficiency in programming languages commonly used in data engineering, such as Python, Scala, or Java, is important. Experience with big data technologies and frameworks, such as Apache Hadoop, Spark, or Kafka, is often required. Strong knowledge of data manipulation, data modeling, ETL (Extract, Transform, Load) processes, and data warehousing concepts is necessary.
• Database Skills: Experience with SQL and proficiency in working with relational databases (e.g., MySQL, PostgreSQL) and/or NoSQL databases (e.g., MongoDB, Cassandra) is important. Knowledge of database design, optimization, and performance tuning is valuable.
• Data Processing and Transformation: Familiarities with data processing and transformation tools and frameworks, such as Apache Beam, Apache Airflow, or Apache Flink, is often desired. Experience with data pipeline development, data streaming, and batch processing is valuable.
• Cloud Platforms: Proficiency in working with cloud platforms, such as AWS, GCP, or Azure, is often required. Knowledge of cloud-based data services, like Amazon Redshift, Google BigQuery, or Azure Data Lake, and experience with cloud-based data storage and processing solutions is important.
• Data Quality and Governance: Understanding data quality principles and experience in implementing data quality checks, data validation, and data governance processes is often necessary. Familiarity with data privacy regulations and compliance, such as GDPR or CCPA, may be required.
• Collaboration and Communication: Excellent communication skills and the ability to work effectively in cross-functional teams are important. Collaboration with data scientists, data analysts, software engineers, and other stakeholders to understand data requirements and deliver robust data solutions is often expected.