Data Engineer - Senior

Leads projects for the design, development, and maintenance of a data and analytics platform. Ensures efficient processing, storage, and availability of data for analysts and other consumers. Collaborates with key business stakeholders, IT experts, and subject-matter experts to plan, design, and deliver optimal analytics and data science solutions. Works on one or multiple product teams simultaneously.

Note:- Even though the role is categorized as Remote, it will follow a hybrid work model.

Key Responsibilities:

• Design and automate the deployment of distributed systems for ingesting and transforming data from various sources (relational, event-based, unstructured).
• Develop frameworks for continuous monitoring and troubleshooting of data quality and integrity issues.
• Implement data governance processes for metadata management, data access, and retention policies for internal and external users.
• Provide guidance on building reliable, efficient, scalable, and quality data pipelines with monitoring and alert mechanisms using ETL/ELT tools or scripting languages.
• Design and implement physical data models to define database structures and optimize performance through indexing and table relationships.
• Optimize, test, and troubleshoot data pipelines.
• Develop and manage large-scale data storage and processing solutions using distributed and cloud-based platforms such as Data Lakes, Hadoop, Hbase, Cassandra, MongoDB, Accumulo, DynamoDB, and others.
• Utilize modern tools and architectures to automate common, repeatable, and tedious data preparation and integration tasks.
• Drive automation in data integration and management by renovating the data management infrastructure.
• Ensure the success of critical analytics initiatives by employing agile development methodologies such as DevOps, Scrum, and Kanban.
• Coach and mentor less experienced team members.

Education, Licenses, Certifications:

• Bachelor s or master s degree in Computer Science, Information Technology, Engineering, or a related field.

Experience:

• 8+ years of experience in data engineering or a related field, with experience in a leadership role.
• Intermediate experience in relevant disciplines is required.
• Knowledge of the latest data engineering technologies and trends is preferred, including:
  • Analyzing complex business systems, industry requirements, and data regulations.
  • Processing and managing large datasets.
  • Designing and developing Big Data platforms using open-source and third-party tools.
  • SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent.
  • SQL query language.
  • Cloud-based clustered compute implementation.
  • Developing applications requiring large file movement in a cloud-based environment.
  • Building analytical solutions.
• Intermediate experience in the following is preferred:
  • IoT technology.
  • Agile software development.

Technical Skills:

• Expert-level proficiency in Spark, including optimization, debugging, and troubleshooting Spark jobs.
• Solid knowledge of Azure Databricks for scalable, distributed data processing.
• Strong coding skills in Python and Scala for data processing.
• Experience with SQL, especially for large datasets.
• Knowledge of data formats such as Iceberg, Parquet, ORC, and Delta Lake.
• Experience developing CI/CD processes.
• Deep understanding of Azure Data Services (e. g. , Azure Blob Storage, Azure Data Lake, Azure SQL Data Warehouse, Synapse Analytics, etc. ).
• Familiarity with data lakes, data warehouses, and modern data architectures.

Competencies:

• System Requirements Engineering - Translates stakeholder needs into verifiable requirements, establishing acceptance criteria and assessing the impact of requirement changes.
• Collaborates - Builds partnerships and works collaboratively with others to meet shared objectives.
• Communicates effectively - Develops and delivers clear, audience-specific communications.
• Customer focus - Builds strong customer relationships and delivers customer-centric solutions.
• Decision quality - Makes timely and informed decisions to keep the organization moving forward.
• Data Extraction - Performs ETL activities from various sources using appropriate tools and technologies.
• Programming - Develops, tests, and maintains computer code and scripts to meet business and compliance requirements.
• Quality Assurance Metrics - Uses IT Operating Model (ITOM) and SDLC standards to assess solution quality.
• Solution Documentation - Documents solutions for improved productivity and knowledge transfer.
• Solution Validation Testing - Ensures configuration changes and solutions meet customer requirements.
• Data Quality - Identifies, understands, and corrects data flaws to enhance information governance.
• Problem Solving - Uses systematic analysis to identify root causes and implement robust solutions.
• Values differences - Recognizes and appreciates diverse perspectives and cultures.