Tential Solutions
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "unknown," offering a pay rate of "unknown," and is fully remote. Key skills include Python, Java, SQL, AWS, Spark, and Databricks, with preferred experience in banking or financial services.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
December 11, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
District of Columbia, United States
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🧠 - Skills detailed
#Cloud #Spark SQL #GIT #SQL (Structured Query Language) #Data Science #PySpark #Python #IAM (Identity and Access Management) #Version Control #Datasets #Code Reviews #Documentation #Complex Queries #Lambda (AWS Lambda) #Databricks #Data Analysis #Delta Lake #Data Processing #Data Engineering #Batch #Scala #Consulting #AWS (Amazon Web Services) #Kafka (Apache Kafka) #Spark (Apache Spark) #S3 (Amazon Simple Storage Service) #Data Modeling #Java #Data Pipeline #"ETL (Extract #Transform #Load)" #Airflow #BI (Business Intelligence)
Role description
Overview
We’re partnering with a Big 4 consulting firm to add a Data Engineer to their team supporting a major banking and credit organization. This role focuses on building and optimizing scalable, cloud-based data pipelines using Python, Java, SQL, AWS, Spark, Databricks, and EMR. You’ll work across consulting and client teams to deliver reliable data solutions that power analytics, risk, and credit decisioning use cases. This position is fully remote.
Responsibilities
• Design, build, and maintain scalable data pipelines and ETL/ELT processes using Python, Java, and SQL.
• Develop and optimize distributed data processing workloads using Spark (batch and/or streaming) on AWS.
• Build and manage data workflows on AWS, leveraging services such as EMR, S3, Lambda, Glue, and related components as appropriate.
• Use Databricks to develop, schedule, and monitor notebooks, jobs, and workflows supporting analytics and data products.
• Implement data models and structures that support banking/credit analytics, reporting, and downstream applications (e.g., risk, fraud, portfolio, customer insights).
• Monitor, troubleshoot, and tune pipeline performance, reliability, and cost in a production cloud environment.
• Collaborate with consultants, client stakeholders, data analysts, and data scientists to understand requirements and translate them into technical solutions.
• Apply best practices for code quality, testing, version control, and CI/CD within the data environment.
• Contribute to documentation, standards, and reusable components to improve consistency and speed across the data engineering team.
Required Qualifications
• Strong hands-on experience with Python and Java for data engineering, ETL/ELT, or backend data services.
• Advanced SQL skills, including complex queries, performance tuning, and working with large, relational datasets.
• Production experience on AWS, ideally with services such as EMR, S3, Lambda, Glue, IAM, and CloudWatch.
• Practical experience building and optimizing Spark jobs (PySpark, Spark SQL, or Scala).
• Hands-on experience with Databricks (notebooks, clusters, jobs, and/or Delta Lake).
• Proven experience building and supporting reliable, performant data pipelines in a modern cloud environment.
• Solid understanding of data warehousing concepts, data modeling, and best practices for structured and semi-structured data.
• Experience working in collaborative engineering environments (Git, code reviews, branching strategies).
• Strong communication skills and comfort working in a consulting/client-facing environment.
Preferred Qualifications (Nice To Have)
• Experience in banking, credit, financial services, or highly regulated environments.
• Background with streaming data (e.g., Spark Streaming, Kafka, Kinesis) and real-time or near–real-time data processing.
• Familiarity with orchestration tools (e.g., Airflow, Databricks jobs scheduler, Step Functions).
• Experience supporting analytics, BI, or data science teams (e.g., building curated datasets, feature stores, or semantic layers).
#Remote
Overview
We’re partnering with a Big 4 consulting firm to add a Data Engineer to their team supporting a major banking and credit organization. This role focuses on building and optimizing scalable, cloud-based data pipelines using Python, Java, SQL, AWS, Spark, Databricks, and EMR. You’ll work across consulting and client teams to deliver reliable data solutions that power analytics, risk, and credit decisioning use cases. This position is fully remote.
Responsibilities
• Design, build, and maintain scalable data pipelines and ETL/ELT processes using Python, Java, and SQL.
• Develop and optimize distributed data processing workloads using Spark (batch and/or streaming) on AWS.
• Build and manage data workflows on AWS, leveraging services such as EMR, S3, Lambda, Glue, and related components as appropriate.
• Use Databricks to develop, schedule, and monitor notebooks, jobs, and workflows supporting analytics and data products.
• Implement data models and structures that support banking/credit analytics, reporting, and downstream applications (e.g., risk, fraud, portfolio, customer insights).
• Monitor, troubleshoot, and tune pipeline performance, reliability, and cost in a production cloud environment.
• Collaborate with consultants, client stakeholders, data analysts, and data scientists to understand requirements and translate them into technical solutions.
• Apply best practices for code quality, testing, version control, and CI/CD within the data environment.
• Contribute to documentation, standards, and reusable components to improve consistency and speed across the data engineering team.
Required Qualifications
• Strong hands-on experience with Python and Java for data engineering, ETL/ELT, or backend data services.
• Advanced SQL skills, including complex queries, performance tuning, and working with large, relational datasets.
• Production experience on AWS, ideally with services such as EMR, S3, Lambda, Glue, IAM, and CloudWatch.
• Practical experience building and optimizing Spark jobs (PySpark, Spark SQL, or Scala).
• Hands-on experience with Databricks (notebooks, clusters, jobs, and/or Delta Lake).
• Proven experience building and supporting reliable, performant data pipelines in a modern cloud environment.
• Solid understanding of data warehousing concepts, data modeling, and best practices for structured and semi-structured data.
• Experience working in collaborative engineering environments (Git, code reviews, branching strategies).
• Strong communication skills and comfort working in a consulting/client-facing environment.
Preferred Qualifications (Nice To Have)
• Experience in banking, credit, financial services, or highly regulated environments.
• Background with streaming data (e.g., Spark Streaming, Kafka, Kinesis) and real-time or near–real-time data processing.
• Familiarity with orchestration tools (e.g., Airflow, Databricks jobs scheduler, Step Functions).
• Experience supporting analytics, BI, or data science teams (e.g., building curated datasets, feature stores, or semantic layers).
#Remote
