Assistant Vice President – Data Engineer

Role Overview

• Designing, building, and maintaining scalable and efficient data pipelines to extract, transform, and load (ETL) data from various sources into Enterprise Data Platform (EDP).
• Designing and implementing data marts, including dimensional modeling, schema design, and optimization techniques.
• Integrating and consolidating data from diverse sources, such as databases, sftp’s, APIs, and streaming platforms, ensuring data quality, consistency, and integrity.
• Creating and maintaining data models, defining data structures, relationships, and data storage requirements, using techniques like entity-relationship diagrams and data flow diagrams.
• Developing data transformation processes, including data cleansing, normalization, aggregation, and enrichment, to prepare data for analytics and reporting.
• Identifying and resolving performance bottlenecks in data processing and storage systems, optimizing query performance, and improving overall data pipeline efficiency.
• Implementing data quality assurance processes, performing data validation, testing data pipelines, and resolving data quality issues.
• Monitoring data pipelines, diagnosing and troubleshooting issues, performing system upgrades and maintenance tasks to ensure data reliability and availability.
• Collaborating with cross-functional teams, including Business Liaisons, analysts, and software engineers, and documenting data engineering processes, workflows, and best practices.Supports training of Harrison Street team members on how to properly organize findings and read data collected.
• Partners with Technology Infrastructure and Support Operations team to identify, design, and implement internal process improvements: e.g., automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability.
• Partners with Technology Infrastructure and Support Operations teams to build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Microsoft Fabric.
• Understands how Harrison Street’s data platform integrates within the overall technical architecture.

Requirements

• Microsoft Certified – Fabric Data Engineer or Azure Data Engineer.
• Experience using Tableau and/or Power BI.
• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Experience building and optimizing Azure data Factory pipelines, architectures, 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.
• Build processes supporting data transformation, data structures, metadata, dependency, and workload management.
• A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
• Strong organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic environment.
• 5+ years of experience in a Data Engineer role. They should also have experience using the following software/tools:
• Experience with Microsoft Fabric or Azure Data Factory.
• Experience with relational SQL databases.
• Experience with Azure cloud services.
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
• Able to compile and organize statistical information retrieved and present findings to management.
• Experience working with private and sensitive personal information.
• Confident in decision making and the ability to explain processes or choices as needed.
• Strong computer skills and ability to use necessary databases and software.
• Ability to complete milestones and work toward multiple deadlines simultaneously.
• Proficient in data warehousing solutions and ETL tools.
• Proficient in database design, data definition, data dictionary, and related concepts.
• Understands Machine Learning.
• Can leverage data APIs.
• Understands app/dev & SDLC.
• Knowledge of algorithms and data structures.

Tech Stack

• Azure
• Cloud
• ETL
• Java
• Python
• Scala
• SDLC
• SQL
• Tableau