Data Engineer

Job Title: Data Engineer

Location: remote

Duration: 1 month - Extendable

Interview: Two rounds of interviews, the first via Teams. Second-round interviews for finalists will be in-person in Harrisburg.

Job Description

The Commonwealth of Pennsylvania 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 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 our next generation of

data initiatives

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 / 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: e.g.

automating manual processes, optimizing data delivery, re-designing 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/or other decision-support

products.

• Work with stakeholders including the 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

our data systems.

Required Qualifications

• Advanced SQL knowledge and proficiency working with relational databases,

query authoring (SQL), as well as working familiarity with a variety of databases.

• Experience building and optimizing 'big data' 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 working with student-level higher education data, including

establishing linkages to other sources (e.g. employment and wage data) to measure

outcomes and/or 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 analytic 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' data stores.

• Strong project management and organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic

Environment

Required Skill:

• Hands-on experience with cloud based data services such as AWS (e.g. S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, DynamoDB)
• Proficiency in object oriented/object function scripting languages: e.g. Python, R, Java, C++, Scala
• Proficiency in relational SQL and NoSQL databases, including Oracle, MS SQL Server, Postgres, Cassandra.
• 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: e.g. Hadoop, Spark, Kafka.
• Experience with data pipeline and workflow management tools: e.g. Azkaban, Luigi, Airflow.
• Experience with data integration services solutions from vendors such as Informatica, MuleSoft, Talend, and TIBCO.
• Experience with stream processing systems: e.g. Storm, Spark-Streaming.