Job Description

Join to apply for the Data Engineer role at Tential Solutions

Were 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 . Youll 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 nearreal?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).

Seniority Level: Entry level. Employment type: Contract. Job function: Information Technology.

Remote: Yes

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