Job Description

Create and maintain optimal data pipeline architecture,
Assemble large, complex data sets that meet functional / non-functional business requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS \'big data\' technologies.
Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.

Data Quality Engineer:
Developing standards for data quality and ensuring that these standards are followed throughout the organization
Evaluating the quality of data received from data sources to ensure that it meets company standards
Creating or updating data models to ensure that data is stored in an organized manner
Working with other departments to establish procedures for data collection and analysis
Creating reports on data quality issues and recommending solutions to problems
Reviewing data to identify patterns or trends that may indicate errors or inconsistencies
Creating and implementing plans to improve data quality by identifying causes of errors or discrepancies and determining effective solutions
Recommending new data collection methods or technologies as needed to improve the quality of data
Consulting with other departments to determine how new data standards will affect their operations

Data Modeller:
Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning).
Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models
Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.
Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.
Hands-on modeling, design, configuration, installation, performance tuning, and sandbox POC.
Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.