Data Engineer

DESCRIPTION

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 data 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.

• System Requirements Engineering: Translate stakeholder needs into verifiable requirements; establish acceptance criteria; track status throughout the system lifecycle; assess impact of changes.
• Collaborates: Build partnerships and work collaboratively with others to meet shared objectives.
• Communicates Effectively: Develop and deliver multi-mode communications that convey a clear understanding of the unique needs of different audiences.
• Customer Focus: Build strong customer relationships and deliver customer-centric solutions.
• Decision Quality: Make good and timely decisions that keep the organization moving forward.
• Data Extraction: Perform ETL activities from various sources and transform them for consumption by downstream applications and users.
• Programming: Create, write, and test computer code, test scripts, and build scripts using industry standards and tools.
• Quality Assurance Metrics: Apply measurement science to assess solution outcomes using ITOM, SDLC standards, tools, metrics, and KPIs.
• Solution Documentation: Document information and solutions based on knowledge gained during product development activities.
• Solution Validation Testing: Validate configuration item changes or solutions using SDLC standards and metrics.
• Data Quality: Identify, understand, and correct data flaws to support effective information governance.
• Problem Solving: Solve problems using systematic analysis processes and industry-standard methodologies.
• Values Differences: Recognize the value that different perspectives and cultures bring to an organization.

• 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.

• Understanding of the ML lifecycle.
• Exposure to Big Data open source technologies.
• Familiarity with clustered compute cloud-based implementations.
• Experience developing applications requiring large file movement for a cloud-based environment.
• Exposure to building analytical solutions and IoT technology.

• Most work will be with stakeholders in the US, with an overlap of 2-3 hours during EST hours as needed.
• This role will be Hybrid.

• 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.
• Experience in Agile Development and 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.