TELECOMMUTE Data Engineering Delivery Manager

This role will be part of the team responsible for developing and deploying Data Engineering Platform and Integration solutions. Primary responsibility will be to work closely with the Data Engineering team, Data Architects and AI Team to implement data solutions for the organization using Python, PySpark and other big data solutions, creating technical specification documents and test plans. Also provide support for the data solutions across the enterprise.

Job Roles And Responsibilities:

• Understand business processes and how they are modeled in various systems.
• Work with business users, technology teams, and executives to understand their data needs to create innovative solutions to fulfil them.
• Design and Implement data structures, workflows, and integrations between enterprise platforms to ensure the accurate and timely execution of business processes.
• Maintain scalable data pipelines to support continuing increases in data volume and complexity.
• Adhere to established best practices on data integration/engineering, as well as the future of our data infrastructure.
• Managing and improving the performance of our database, queries, tools, and solutions.
• Creating and maintaining data warehouse, data lakes, databases, tables, SQL queries, and ingestion pipelines, predictive models, and downstream analysis.
• Writing complex and efficient queries to transform raw data sources into easily accessible models for our teams and reporting platforms.
• Prepare data for predictive and prescriptive modeling.
• Identify and analyze data patterns.
• Identify ways to improve data reliability, efficiency and quality.
• Work with analytics, data science, and wider engineering teams to help with automating data analysis and visualization needs, advise on transformation processes to populate data models, and explore ways to design and develop data infrastructure.
• Collaborate, coordinate, and communicate across disciplines and departments.
• Ensure compliance with HIPAA regulations and requirements.
• Demonstrate Company’s Core Competencies and values held within.
• Please note due to the exposure of PHI sensitive data - this role is considered to be a High Risk and priveleged Role.
• The position responsibilities outlined above are in no way to be construed as all encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.

• Minimum Gradution Degree and 12+ years’ related experience, three (3) of which should be inclusive of experience with schema designing, data modeling, designing, building, and maintaining data processing systems. Bachelors’ degree in computer science, information technology or a similarly relevant field is highly preferred.
• Required licensures, professional certifications, and/or Board certifications as applicable
• Experience with advanced analytics tools for Object-Oriented/object function scripting using languages such as Python, PySpark, Java, others.
• Database development experience using ETL Process, SQL, SPARK, or BigQuery and experience with Delta Lakes and Data Warehouse , use of Databricks or Snowflakes or similar products.
• Experience in triaging data issues, analyzing end-to-end data pipelines and working with business users in resolving issues.
• Experience in working with data governance/data quality and data security teams and specifically data stewards and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification.
• Exposure to Big Data Development using various tools & techniques Databricks, Snowflakes, OCI, Hive, Impala, Spark, etc.
• Exposure to machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics
• Exposure to agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines.
• Excellent communication skills (verbal, listening and written).
• Ability to build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management.
• Ability to work with both IT and business in integrating analytics and data science output into business processes and workflows.
• An agile learner who brings strong problem-solving skills and enjoys working as part of a technical, cross functional team to solve complex data problems.
• Able to prioritize and manage multiple projects and requests at any one time.
• Strong attention to detail when identifying data relationships, trends, and anomalies.
• Thinking through long-term impacts of key design decisions and handling failure scenarios.
• Ability to effectively share technical information, communicate technical issues and solutions to all levels of business.
• Ability to meet strict deadlines, work on multiple tasks and work well under pressure.