Enterprise Solutions Inc.
Senior Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer with a contract length of over 6 months, offering a competitive pay rate. Key skills include Python, PySpark, Databricks, and experience with AWS. A Bachelor’s degree and 2+ years of relevant experience are required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
680
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🗓️ - Date
December 5, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
-
🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#PySpark #Snowflake #Data Science #Monitoring #Big Data #Collibra #Spark (Apache Spark) #Cloud #Strategy #AWS (Amazon Web Services) #Airflow #Security #Redshift #Agile #S3 (Amazon Simple Storage Service) #Databricks #Databases #Unit Testing #Data Pipeline #Python #NoSQL #"ETL (Extract #Transform #Load)" #Computer Science #Jupyter #Data Warehouse #Data Engineering #Automation
Role description
Qualifications:
• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent experience.
• 2+ years’ experience with tools such as Databricks, Collibra, and Starburst.
• 3+ years’ experience with Python and PySpark.
• Experience using Jupyter notebooks, including coding and unit testing.
• Recent accomplishments working with relational and NoSQL data stores, methods, and approaches (STAR, Dimensional Modeling).
• 2+ years of experience with a modern data stack (Object stores like S3, Spark, Airflow, Lakehouse architectures, real-time databases) and cloud data warehouses such as RedShift, Snowflake.
• Overall data engineering experience across traditional ETL & Big Data, either on-prem or Cloud.
• Data engineering experience in AWS (any CFS2/EDS) highlighting the services/tools used.
• Experience building end-to-end data pipelines to ingest and process unstructured and semi-structured data using Spark architecture.
Additional Requirements:
• This position requires access to confidential supervisory information, which is limited to “Protected Individuals.” Protected Individuals include, but are not limited to, U.S. citizens and U.S. nationals, U.S. permanent residents who are not yet eligible to apply for naturalization, and U.S. permanent residents who have applied for naturalization within six months of being eligible to do so or who will sign a declaration of intent to apply for naturalization before they begin employment.
Responsibilities: Key Responsibilities:
• Design, develop, and maintain robust and efficient data pipelines to ingest, transform, catalog, and deliver curated, trusted, and quality data from disparate sources into our Common Data Platform.
• Actively participate in Agile rituals and follow Scaled Agile processes as set forth by the CDP Program team.
• Deliver high-quality data products and services following Safe Agile Practices.
• Proactively identify and resolve issues with data pipelines and analytical data stores.
• Deploy monitoring and alerting for data pipelines and data stores, implementing auto-remediation where possible to ensure system availability and reliability.
• Employ a security-first, testing, and automation strategy, adhering to data engineering best practices.
• Collaborate with cross-functional teams, including product management, data scientists, analysts, and business stakeholders, to understand their data requirements and provide them with the necessary infrastructure and tools.
• Keep up with the latest trends and technologies, evaluating and recommending new tools, frameworks, and technologies to improve data engineering processes and efficiencies.
Qualifications:
• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent experience.
• 2+ years’ experience with tools such as Databricks, Collibra, and Starburst.
• 3+ years’ experience with Python and PySpark.
• Experience using Jupyter notebooks, including coding and unit testing.
• Recent accomplishments working with relational and NoSQL data stores, methods, and approaches (STAR, Dimensional Modeling).
• 2+ years of experience with a modern data stack (Object stores like S3, Spark, Airflow, Lakehouse architectures, real-time databases) and cloud data warehouses such as RedShift, Snowflake.
• Overall data engineering experience across traditional ETL & Big Data, either on-prem or Cloud.
• Data engineering experience in AWS (any CFS2/EDS) highlighting the services/tools used.
• Experience building end-to-end data pipelines to ingest and process unstructured and semi-structured data using Spark architecture.
Additional Requirements:
• This position requires access to confidential supervisory information, which is limited to “Protected Individuals.” Protected Individuals include, but are not limited to, U.S. citizens and U.S. nationals, U.S. permanent residents who are not yet eligible to apply for naturalization, and U.S. permanent residents who have applied for naturalization within six months of being eligible to do so or who will sign a declaration of intent to apply for naturalization before they begin employment.
Responsibilities: Key Responsibilities:
• Design, develop, and maintain robust and efficient data pipelines to ingest, transform, catalog, and deliver curated, trusted, and quality data from disparate sources into our Common Data Platform.
• Actively participate in Agile rituals and follow Scaled Agile processes as set forth by the CDP Program team.
• Deliver high-quality data products and services following Safe Agile Practices.
• Proactively identify and resolve issues with data pipelines and analytical data stores.
• Deploy monitoring and alerting for data pipelines and data stores, implementing auto-remediation where possible to ensure system availability and reliability.
• Employ a security-first, testing, and automation strategy, adhering to data engineering best practices.
• Collaborate with cross-functional teams, including product management, data scientists, analysts, and business stakeholders, to understand their data requirements and provide them with the necessary infrastructure and tools.
• Keep up with the latest trends and technologies, evaluating and recommending new tools, frameworks, and technologies to improve data engineering processes and efficiencies.
