Data Engineer & Insights Analyst

Location: Harrisburg, PA Remote
Duration: 6+ months
Type: Contract

******Interview Process: Two rounds of interviews. First round via Microsoft Teams; second-round interviews for finalists will be in person in Harrisburg, PA******

Job Description

The Client is looking for a Data Engineer & Insights Analyst to join its team of analytics professionals to support the Pennsylvania Longitudinal Data System (PALDS).

This position will be responsible for transforming complex data into trusted, usable information to support program delivery, operational improvements, and executive decision-making in state government agencies. The Data Engineer & Insights Analyst will understand policy-relevant research questions, evaluate available data sources, and develop analytics products to deliver actionable insights.

Additionally, the Data Engineer & Insights Analyst will design and build data pipelines, integrate and transform data from multiple sources, identify and address data quality issues, and establish controls to support responsible use of data.

This role requires both technical depth and strong analytical judgment, including the ability to work with large, complex, and confidential datasets.

The successful candidate must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. They must also be comfortable with supporting data conversions associated with system modernization and/or replacement efforts.

The right candidate will be excited by the prospect of optimizing the Commonwealth's longitudinal data architecture to support its next generation of data initiatives.

Key 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 tools such as 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, design, and implement internal process improvements, including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability.

Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and big data technologies.

Build analytics tools that utilize the data pipeline to provide actionable insights into education and workforce outcomes, operational efficiency, and other key 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 stakeholders, including Executive, Data, Design, and Support teams, to assist with data-related technical issues and support their data infrastructure needs.

Ensure compliance with applicable Commonwealth policies, data governance standards, privacy requirements, security controls, and interagency data-sharing agreements.

Create data tools for analytics and data scientist team members that assist them in building and optimizing data products needed to support ongoing operations and data-informed decision-making.

Work with data and analytics professionals to strive for greater functionality in data systems.

Required Skills and Qualifications

• Hands-on experience with cloud-based data services such as AWS S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, and DynamoDB.
• Proficiency in object-oriented/object-function scripting languages such as Python, R, Java, C++, and Scala.
• Proficiency in relational SQL and NoSQL databases, including Oracle, MS SQL Server, PostgreSQL, and Cassandra.
• Advanced SQL knowledge and query authoring skills.
• Experience with version control systems such as Git and CI/CD pipelines.
• Knowledge of data modeling techniques, including dimensional modeling, data vault, and star/snowflake schemas.
• Familiarity with data cataloging and metadata management tools such as AWS Glue Data Catalog.
• Understanding of data governance, data lineage, and data quality frameworks.
• Knowledge of REST API design and integration patterns.
• Experience with big data tools such as Hadoop, Spark, and Kafka.
• Experience with data pipeline and workflow management tools such as Azkaban, Luigi, and Airflow.
• Experience with data integration solutions from vendors such as Informatica, MuleSoft, Talend, and TIBCO.
• Experience with stream-processing systems such as Storm and Spark Streaming.

Desired Qualifications

Advanced knowledge and proficiency in designing, building, and refining dashboards, reports, analytical models, and decision-support products for executive and non-technical users.

Experience working with student-level higher education data, including establishing linkages to other sources (e.g., employment and wage data) to measure outcomes and generate insights for decision-making.

Experience performing root-cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.

Strong analytical skills related to working with unstructured datasets.

Experience building processes supporting data transformation, data structures, metadata, dependency, 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.