Senior AWS Data Engineer - Python, SQL & Big Data

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.

• Skill

Required / Desired

Years Used

Last Used

Hands-on experience with cloud-based data services such as AWS (e.g. S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, DynamoDB)

Required

Proficiency in object-oriented/object function scripting languages: e.g. Python, R, Java, C , Scala

Required

Proficiency in relational SQL and NoSQL databases, including Oracle, MS SQL Server, Postgres, Cassandra.

Required

Experience with version control systems such as Git and CI/CD pipelines

Required

Knowledge of data modeling techniques including dimensional modeling, data vault, and star/snowflake schemas.

Required

Familiarity with data cataloging and metadata management tools such as AWS Glue Data Catalog.

Required

Understanding of data governance, data lineage, and data quality frameworks.

Required

Knowledge of REST API design and integration patterns.

Required

Experience with big data tools: e.g. Hadoop, Spark, Kafka.

Required

Experience with data pipeline and workflow management tools: e.g. Azkaban, Luigi, Airflow.

Required

Experience with data integration services solutions from vendors such as Informatica, MuleSoft, Talend, and TIBCO.

Required

Experience with stream-processing systems: e.g. Storm, Spark-Streaming.

Required