Senior Data Engineer

The BIA Senior Data Engineer designs and leads the implementation of data engineering solutions in the Business Intelligence and Analytics team.

Responsible for design, development, optimization, and maintenance of robust and scalable data engineering pipelines. Ensure data and analytical solutions meet Business Requirements and best practices. Collaborate with data architects, data scientists and other stakeholders to provide scalable analytical solutions for the organization. Continuously improve and optimize existing ETL processes adhering to data privacy and best practices. Document all data engineering processes and architecture.

The position also requires staying abreast with changes in technology, programming languages, and software development tools.

Key Responsibilities & Duties:

Data Modeling/Designing Datasets (20%): Designs, implements, and maintains complex data engineering solutions to acquire and prepare data to support reporting, BI, analytics and data science use cases. Plans and drives the development of data engineering solutions ensuring that solutions balance functional and non-functional requirements.

Data Architecture and Technical Infrastructure (20%): Plans and drives the development of data engineering solutions ensuring that solutions balance functional and non-functional requirements. Monitors application of data standards and architectures including security and compliance.

Data Pipeline/ETL (15%): Develops various functional components of the data warehouse including ETL processes, data cleansing, system management, load automation, data acquisitions, and exception handling. Creates and maintains data pipelines to connect data within and between data stores, applications and organizations.

SDLC Methodology & Project Management (10%): Plans and contributes to technical transitions between development, testing, and production phases of solutions' lifecycle, and the facilitation of the change control, problem management, and communication processes.

Innovation, Continuous Improvement & Optimization (10%): Provides advice and guidance to others using the data structures and associated components.

Support & Operations (5%): Manages monitoring, job control and production support. Works with other team members, DBA team and other teams to resolve issue.

Data Governance and Data Quality (5%): Carries out complex data quality checking and remediation and Explore ways to enhance data quality and reliability. Makes recommendations for improving data management and development of data policies.

Metadata Management & Documentation (5%): Contributes to organizational policies, standards, and guidelines for data engineering. Develops metadata content to document data assets and data pipelines.

End-User Support, Education and Enablement (5%): Plans and facilitates training and Data Literacy initiatives within the team and End user community.

Partnership and Community Building (5%): Collaborates with other IT teams, business community, data scientists and other architects to meet business requirements. Interact with DBAs on data designs optimal for data engineering solutions performance

Skills and Requirements:

1. Development, maintenance, and enhancement of Data Pipelines (ETL/ELT) and processes with thorough knowledge of star/snowflake schemas
2. Developing complex SQL queries and SQL optimization
3. Development experience must be full Life Cycle experience including business requirements gathering, data sourcing, testing/data reconciliation, and deployment within Business Intelligence/Data Warehousing Architecture.
4. Designing and implementing data security
5. Monitoring and optimizing data storage and data processing
6. Use of Object function/object-oriented scripting languages including Scala, C++, Java and Python
7. Cloud data warehouse and data lake platforms (e.g., Snowflake, Databricks, Redshift, BigQuery, MS Synapse, MS ADLS, Apache Haddop, etc.)

Specific Skills and/or Business Competencies

• Developing data services that are fit for purpose, resilient, scalable, and future proof to meet user needs
• Advanced SQL knowledge and experience working with relational databases and working familiarity with various databases.
• Demonstrated understanding and experience using software and tools including ETL, relational SQL and NoSQL databases, big data tools like Kafka, Spark, and Hadoop
• Experience building and optimizing big data pipelines, architecture, and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Experience and knowledge of project management best practices and agile software development methodologies
• A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
• Working knowledge of message queuing, stream processing, and highly scalable big data data stores.
• Strong project management and organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic environment.
• Understanding of data science concepts

Familiarity with AI/ML frameworks and libraries