Senior Data Engineer

Position Summary... Demonstrates up-to-date expertise and applies this to the development, execution, and improvement of action plans by providing expert advice and guidance to others in the application of information and best practices; supporting and aligning efforts to meet customer and business needs; and building commitment for perspectives and rationales. Provides and supports the implementation of business solutions by building relationships and partnerships with key stakeholders; identifying business needs; determining and carrying out necessary processes and practices; monitoring progress and results; recognizing and capitalizing on improvement opportunities; and adapting to competing demands, organizational changes, and new responsibilities.

Models compliance with company policies and procedures and supports company mission, values, and standards of ethics and integrity by incorporating these into the development and implementation of business plans; using the Open Door Policy; and demonstrating and assisting others with how to apply these in executing business processes and practices.

Tech. Problem Formulation Requires knowledge of: Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. To translate/ co-own business problems within ones discipline to data related or mathematical solutions. Identifies appropriate methods/tools to be leveraged to provide a solution for the problem. Shares use cases and gives examples to demonstrate how the method would solve the business problem. Understanding Business Context Requires knowledge of: Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices;

Directly relevant business metrics and business areas. To provide recommendations to business stakeholders to solve complex business issues. Develops business cases s for projects with a projected return on investment or cost savings. Translates business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact. Serves as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work. Data Source Identification Requires knowledge of: Functional business domain and scenarios; Categories of data and where it is held;

Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. To support the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for purpose. Performs initial data quality checks on extracted data. Data Transformation and Integration

Requires knowledge of:

Internal and external data sources including how they are collected, where and how they are stored, and interrelationships, both within and external to the organization; Techniques like ETL batch processing, streaming ingestion, scrapers, API and crawlers; Data warehousing service for structured and semi-structured data, or to MPP databases such as Snowflake, Microsoft Azure, Presto or Google BigQuery; Pre-processing techniques such as transformation, integration, normalization, feature extraction, to identify and apply appropriate methods; Techniques such as decision trees, advanced regression techniques such as LASSO methods, random forests etc; Cloud and big data environments like EDO2 systems. To extract data from identified databases.

Creates data pipelines and transform data to a structure that is relevant to the problem by selecting appropriate techniques. Develops knowledge of current data science and analytics trends. Data Modeling Requires knowledge of: Cloud data strategy, data warehouse, data lake, and enterprise big data platforms; Data modeling techniques and tools (For example, Dimensional design and scalability), Entity Relationship diagrams, Erwin, etc. ; Query languages SQL / NoSQL;

Data flows through the different systems; Tools supporting automated data loads; Artificial Intelligent - enabled metadata management tools and techniques. To analyze complex data elements, systems, data flows, dependencies, and relationships to contribute to conceptual, physical, and logical data models. Develops the Logical Data Model and Physical Data Models including data warehouse and data mart designs. Defines relational tables, primary and foreign keys, and stored procedures to create a data model structure. Evaluates existing data models and physical databases for variances and discrepancies.

Develops efficient data flows. Analyzes data-related system integration challenges and proposes appropriate solut Code Development and Testing Requires knowledge of: Coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding.

To write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements. Creates test cases to review and validate the proposed solution design.

Creates proofs of concept. Tests the code using the appropriate testing approach. Deploys software to production servers. Contributes code documentation, maintains playbooks, and provides timely progress updates. Data Governance Requires knowledge of: Data value chains; Data processes and practices; Regulatory and ethical requirements around data; Data modeling, storage, integration, and warehousing;

Data value chains (identification, ingestion, processing, storage, analysis, and utilization);

Data quality framework and metrics; Regulatory and ethical requirements around data privacy, security, storage, retention, and documentation; Business implications on data usage;

Data Strategy; Enterprise regulatory and ethical policies and strategies. To establish, modify, and document data governance projects and recommendations. Implements data governance practices in partnership with business stakeholders and peers. Interprets company and regulatory policies on data. Educates others on data governance processes, practices, policies, and guidelines.

Provides recommendations on needed updates or inputs into data governance policies, practices, or guidelines. Data Strategy Requires knowledge of: Understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability etc; Understanding of the methods and applications that unlock the monetary value of data assets. To understand, articulate, and apply principles of the defined strategy to routine business problems that involve a single function.