Eiso-Palds-Fa2 A4 Sc3

Wiserhunt•Harrisburg, PA

2d•Remote

About The Position

The Commonwealth of Pennsylvania is seeking a Data Engineer & Insights Analyst to support the Pennsylvania Longitudinal Data System (PALDS). The role transforms complex data into trusted, usable information that supports program delivery, operational improvements, and executive decision-making across state government agencies. The analyst will interpret policy-relevant research questions, assess available data sources, and develop analytics products that provide actionable insights. The position designs and builds data pipelines; integrates and transforms data from multiple sources; identifies and addresses data-quality issues; and establishes controls for responsible data use. It requires technical depth and sound analytical judgment, including the ability to work with large, complex, confidential datasets and support the needs of multiple teams, systems, and products. The role also supports data conversions associated with system modernization or replacement and helps optimize the Commonwealth’s longitudinal data architecture for future data initiatives. Work is remote. Finalists are expected to attend in-person second-round interviews in Harrisburg; the first interview is via Teams. The listed assignment dates are November 23, 2026, through December 31, 2026.

Requirements

Advanced SQL knowledge and proficiency with relational databases, query authoring, and familiarity with a variety of databases.
Experience building and optimizing big-data pipelines, architectures, and data sets.
Advanced knowledge and proficiency designing, building, and refining dashboards, reports, analytical models, and decision-support products for executive and non-technical users.
Experience with student-level higher-education data, including linking it to sources such as employment and wage data to measure outcomes or support decision-making.
Experience performing root-cause analysis on internal and external data and processes to answer business questions and identify improvement opportunities.
Strong analytical skills working with unstructured datasets.
Experience building processes for data transformation, data structures, metadata, dependency management, and workload management.
A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
Working knowledge of message queuing, stream processing, and highly scalable big-data stores.
Strong project-management and organizational skills.
Experience supporting and working with cross-functional teams in a dynamic environment.
Five years of hands-on experience with cloud-based data services such as AWS S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, and DynamoDB.
Five years of proficiency in object-oriented or object-function scripting languages such as Python, R, Java, C++, or Scala.
Five years of proficiency with relational SQL and NoSQL databases, including Oracle, MS SQL Server, PostgreSQL, and Cassandra.
Five years of experience with version-control systems such as Git and CI/CD pipelines.
Five years of knowledge of data-modeling techniques, including dimensional modeling, data vault, and star/snowflake schemas.
Five years of familiarity with data-cataloging and metadata-management tools such as AWS Glue Data Catalog.
Five years of understanding of data governance, data lineage, and data-quality frameworks.
Five years of knowledge of REST API design and integration patterns.
Five years of experience with big-data tools such as Hadoop, Spark, and Kafka.
Five years of experience with data-pipeline and workflow-management tools such as Azkaban, Luigi, and Airflow.
Five years of experience with data-integration services from vendors such as Informatica, MuleSoft, Talend, and TIBCO.
Five years of experience with stream-processing systems such as Storm and Spark Streaming.

Responsibilities

Create and maintain optimal data pipeline architecture.
Design and implement cloud-based data solutions using AWS services such as S3, Glue, Redshift, EMR, Athena, Kinesis, and Lambda.
Build and maintain data orchestration workflows using Apache Airflow, AWS Step Functions, or similar services.
Assemble large, complex data sets that meet functional and non-functional business-area requirements.
Engineer analytical data environments that support advanced statistical analysis, predictive modeling, machine learning, and evidence-building activities.
Partner with data scientists, research analysts, and policy analysts to ensure enterprise data assets support reproducible research and statistical modeling.
Identify and implement internal process improvements, including automation, optimized data delivery, and scalable infrastructure redesign.
Build infrastructure for extracting, transforming, and loading data from varied sources using SQL and big-data technologies.
Build analytics tools that provide actionable insights into education and workforce outcomes, operational efficiency, and other business performance metrics.
Develop and maintain data-pipeline monitoring, alerting, and logging frameworks.
Implement data-quality checks and validation frameworks across pipelines.
Develop dashboards, reports, analytical models, and other decision-support products.
Work with Executive, Data, Design, and Support teams on data-related technical issues and data-infrastructure needs.
Ensure compliance with Commonwealth policies, data-governance standards, privacy requirements, security controls, and interagency data-sharing agreements.
Create analytics and data-science tools that help team members build and optimize data products for operations and data-informed decision-making.
Work with data and analytics professionals to improve the functionality of data systems.
Support data conversions associated with system modernization or replacement efforts.