Metropolitan Transportation Authority-posted about 1 month ago
$110,000 - $123,000/Yr
Full-time • Mid Level
Hybrid • New York, NY
5,001-10,000 employees
Executive, Legislative, and Other General Government Support

The Metropolitan Transportation Authority is North America's largest transportation network, serving a population of 15.3 million people across a 5,000-square-mile travel area surrounding New York City, Long Island, southeastern New York State, and Connecticut. The MTA network comprises the nation's largest bus fleet and more subway and commuter rail cars than all other U.S. transit systems combined. MTA strives to provide a safe and reliable commute, excellent customer service, and rewarding opportunities. This position will report to the Director of HR Data Science and support the day-to-day data pipeline initiatives to design, build, and maintain ease for data structures to facilitate reporting and monitor key performance indicators. The incumbent will collaborate across Human Capital Management disciplines to identify internal/external data sources to design table structure, define ETL strategy, automate quality assurance checks, and implement scalable ETL solutions.

  • Ensure that all assignments are completed with the highest quality and within agreed-upon Service Level agreement guidelines and Key Performance Indicator (KPI) targets.
  • Create and conduct a project/architecture design review.
  • Proficient knowledge of Azure Data Factory, Databricks, & Azure Delta Lake.
  • Experience programming languages (e.g., Python, R).
  • Working experience in data extraction using API.
  • Develop HR data pipelines and reports with advanced SQL programming language and maintain Data Warehousing/Data Lakes/Data Hubs and Analytical reporting Environment.
  • Work with IT teams to collect required data from internal and external systems and troubleshoot HR system issues.
  • Design and build modern data management solutions and create POC when necessary to test new approaches.
  • Create runbooks and actionable alerts as part of the development process.
  • Perform SQL and ETL tuning as necessary.
  • Perform ad hoc analysis as necessary.
  • Identify and implement continuous improvement initiatives as assigned.
  • Other duties as assigned.
  • Strong understanding of data modeling principles, including Dimensional modeling, data normalization principles, etc.
  • Proficient understanding of SQL Engines and able to develop advanced queries and analytics
  • Familiarity with data exploration/data visualization tools like MS Power BI, PeopleSoft HCM, JobVite, JDXpert, Jetdocs, or Oracle Analytics CloudAbility to think strategically, analyze, and interpret Human Capital Management and Financial data.
  • Strong communication skills - written and verbal presentations.
  • Excellent conceptual and analytical reasoning competencies.
  • Comfortable working in a fast-paced and highly collaborative environment.
  • Process-oriented with excellent documentation skills, including strong Excel & PowerPoint skills, Visio flows, and mock-up creation.
  • Bachelor's degree in Computer Science, Information Management, Statistics, or Finance or related field. An equivalent combination of education and experience may be considered in lieu of a degree.
  • A minimum of four (4) years of relevant professional experience leading, implementing, and reporting on business key performance indicators in a data warehousing/lake/hub environment.
  • Minimum of four (4) years of experience using SQL for analytics, working with traditional relational databases and/or distributed systems such as PeopleSoft Enterprise Performance Management (EPM), Hadoop / Hive, BigQuery, Redshift, or Oracle Databases.
  • Master's Degree in Computer Science, Information Management, or Statistics
  • Experience programming languages (e.g., Python, R) preferred.
  • Minimum of four (4) years of management experience
  • Minimum of one (1) year of experience with workflow management tools (Airflow, Oozie, Azkaban, UC4)
  • Understanding of Finance Data and Human Resource Data practices and procedures.
  • Proficient Knowledge of Data Repositories/warehouses/Lakes/Hubs.
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