Data Engineer

Although the role category specified in the GPP is Remote, the requirement is for Hybrid. Key Responsibilities:

• Implement and automate deployment of distributed systems for ingesting and transforming data from various sources (relational, event-based, unstructured).
• Continuously monitor and troubleshoot data quality and integrity issues.
• Implement data governance processes and methods for managing metadata, access, and retention for internal and external users.
• Develop reliable, efficient, scalable, and quality data pipelines with monitoring and alert mechanisms using ETL/ELT tools or scripting languages.
• Develop physical data models and implement data storage architectures as per design guidelines.
• Analyze complex data elements and systems, data flow, dependencies, and relationships to contribute to conceptual, physical, and logical data models.
• Participate in testing and troubleshooting of data pipelines.
• Develop and operate large-scale data storage and processing solutions using distributed and cloud-based platforms (e. g. , Data Lakes, Hadoop, Hbase, Cassandra, MongoDB, Accumulo, DynamoDB).
• Use agile development technologies, such as DevOps, Scrum, Kanban, and continuous improvement cycles, for data-driven applications.

Must-Have:

• 3-5 years of experience in data engineering with a strong background in Azure Databricks and Scala/Python.
• Hands-on experience with Spark (Scala/PySpark) and SQL.
• Experience with SPARK Streaming, SPARK Internals, and Query Optimization.
• Proficiency in Azure Cloud Services.
• Agile Development experience.
• Unit Testing of ETL.
• Experience creating ETL pipelines with ML model integration.
• Knowledge of Big Data storage strategies (optimization and performance).
• Critical problem-solving skills.
• Basic understanding of Data Models (SQL/NoSQL) including Delta Lake or Lakehouse.
• Quick learner.

Nice-to-Have:

• Understanding of the ML lifecycle.
• Exposure to Big Data open-source technologies.
• Experience with SPARK, Scala/Java, Map-Reduce, Hive, HBase, and Kafka.
• SQL query language proficiency.
• Experience with clustered compute cloud-based implementations.
• Familiarity with developing applications requiring large file movement for a cloud-based environment.
• Exposure to Agile software development.
• Experience building analytical solutions.
• Exposure to IoT technology.

• Relevant experience preferred, such as working in temporary student employment, internships, co-ops, or other extracurricular team activities.
• Knowledge of the latest technologies in data engineering is highly preferred, including:
  • Exposure to Big Data open source
  • SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework
  • SQL query language
  • Clustered compute cloud-based implementation experience
  • Familiarity with developing applications requiring large file movement for a cloud-based environment
  • Exposure to Agile software development
  • Exposure to building analytical solutions
  • Exposure to IoT technology

Most of the work will be with stakeholders in the US, with an overlap of 2-3 hours during EST hours on a need basis.

• College, university, or equivalent degree in a relevant technical discipline, or relevant equivalent experience required.
• This position may require licensing for compliance with export controls or sanctions regulations.

• System Requirements Engineering: Uses appropriate methods and tools to translate stakeholder needs into verifiable requirements.
• Collaborates: Building partnerships and working collaboratively with others to meet shared objectives.
• Communicates effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.
• Customer focus: Building strong customer relationships and delivering customer-centric solutions.
• Decision quality: Making good and timely decisions that keep the organization moving forward.
• Data Extraction: Performs ETL activities from various sources and transforms them for consumption by downstream applications and users.
• Programming: Creates, writes, and tests computer code, test scripts, and build scripts using industry standards and tools.
• Quality Assurance Metrics: Applies measurement science to assess whether a solution meets its intended outcomes.
• Solution Documentation: Documents information and solutions based on knowledge gained during product development activities.
• Solution Validation Testing: Validates configuration item changes or solutions using defined best practices.
• Data Quality: Identifies, understands, and corrects flaws in data to support effective information governance.
• Problem Solving: Solves problems using systematic analysis processes and industry-standard methodologies.
• Values differences: Recognizing the value that different perspectives and cultures bring to an organization.