Data Engineer & Insights Analyst

SMART TECH SKILLS LLC•Sinking Spring, PA

2h•Remote

About The Position

The Data Engineer & Insights Analyst transforms complex data into trusted, usable information that supports program delivery, operational improvements, and executive decision-making. The role designs and builds cloud-based data pipelines, integrates multiple data sources, addresses data quality issues, and develops dashboards, reports, and analytical products from large, confidential datasets. The position partners with data scientists, research and policy analysts, and cross-functional stakeholders, and supports data conversions tied to system modernization efforts. The role builds and maintains cloud data solutions and pipeline infrastructure, ensures data quality, monitoring, governance, privacy, and security, and develops analytics products that support education and workforce outcomes, operational efficiency, statistical analysis, predictive modeling, machine learning, and evidence-building. It also supports reproducible research, resolves data-related technical issues, and helps analysts and data scientists build and optimize data products. The position is remote, with flexible work-from-home options.

Requirements

At least 5 years of hands-on experience with cloud-based data services such as AWS S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, and DynamoDB.
At least 5 years of proficiency in object-oriented or functional scripting languages such as Python, R, Java, C++, or Scala.
At least 5 years of proficiency in relational SQL and NoSQL databases, including Oracle, MS SQL Server, Postgres, and Cassandra, with advanced SQL and query-authoring skills.
At least 5 years of experience with version control systems such as Git and CI/CD pipelines.
At least 5 years of knowledge of data modeling techniques, including dimensional modeling, data vault, and star/snowflake schemas.
At least 5 years of familiarity with data cataloging and metadata management tools such as AWS Glue Data Catalog.
At least 5 years of understanding of data governance, data lineage, and data quality frameworks.
At least 5 years of knowledge of REST API design and integration patterns.
At least 5 years of experience with big data tools such as Hadoop, Spark, and Kafka.
At least 5 years of experience with data pipeline and workflow management tools such as Azkaban, Luigi, and Airflow.
At least 5 years of experience with data integration solutions from vendors such as Informatica, MuleSoft, Talend, and TIBCO.
At least 5 years of experience with stream-processing systems such as Storm and Spark-Streaming.
Experience building and optimizing big data pipelines, architectures, and data sets, including data transformation, data structures, metadata, dependency, and workload management.
Advanced proficiency designing, building, and refining dashboards, reports, analytical models, and decision-support products for executive and non-technical users.
Experience performing root-cause analysis on internal and external data and processes to answer business questions and identify improvement opportunities.
Strong analytic skills with unstructured datasets and a history of extracting value from large, disconnected datasets.
Working knowledge of message queuing, stream processing, and highly scalable big data stores.
Ability to work in a self-directed manner while supporting the data needs of multiple teams, systems, and products.
Strong analytical judgment when working with large, complex, and confidential datasets.
Strong project management and organizational skills.
Ability to collaborate effectively with cross-functional teams in a dynamic environment.
Ability to communicate insights clearly to executive and non-technical audiences.
Commitment to continuous improvement of data systems and functionality.

Nice To Haves

Experience with student-level higher education data and linkages to employment and wage data.
Experience supporting data conversions associated with system modernization or replacement efforts.

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.
Build infrastructure to extract, transform, and load data from varied sources using SQL and big data technologies.
Assemble large, complex data sets that meet functional and non-functional business requirements.
Develop and maintain pipeline monitoring, alerting, and logging frameworks.
Implement data quality checks and validation frameworks across pipelines.
Ensure compliance with applicable policies, data governance standards, privacy requirements, security controls, and data-sharing agreements.
Automate manual processes, optimize data delivery, and redesign infrastructure for greater scalability.
Develop dashboards, reports, analytical models, and other decision-support products.
Build analytics tools that provide insights into education and workforce outcomes, operational efficiency, and key performance metrics.
Engineer analytical data environments supporting statistical analysis, predictive modeling, machine learning, and evidence-building activities.
Create data tools that help analysts and data scientists build and optimize data products.
Partner with data scientists, research analysts, and policy analysts to support reproducible research and statistical modeling.
Work with executive, data, design, and support teams to resolve data-related technical issues and meet data infrastructure needs.
Support data conversions associated with system modernization or replacement efforts.
Perform root-cause analysis on internal and external data and processes to answer business questions and identify improvement opportunities.