Senior Data Engineer – Python
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What you'll do
- Design, develop, and maintain scalable Python-based data pipelines for ingestion, transformation, and distribution of data across enterprise platforms.
- Build reusable Python libraries, frameworks, and automation utilities to support data engineering workflows.
- Develop and optimize complex SQL queries, stored procedures, and database objects across multiple relational database platforms.
- Design and implement ETL/ELT solutions including staging frameworks, incremental processing, data reconciliation, and error handling.
- Develop and maintain logical and physical data models to support both operational and analytical use cases.
- Work with NoSQL databases (e.g., MongoDB) to design data structures and access patterns suited to non-relational workloads.
- Build and maintain data pipelines using PySpark for large-scale distributed data processing where applicable.
- Implement data quality, validation, and completeness controls to ensure accuracy and reliability across data products.
- Collaborate with data scientists, analysts, software engineers, and business stakeholders to understand requirements and deliver high-quality data solutions.
- Contribute to CI/CD pipelines for automated testing, deployment, and version control of data workflows.
- Participate in code reviews, troubleshooting, debugging, and production support of data systems.
- Support data governance, security standards, and compliance requirements across all data assets.
What they're looking for
- Strong proficiency in Python for data engineering, including data transformation, orchestration, workflow automation, and utility development.
- Expert-level SQL skills across relational databases (PostgreSQL, Oracle, SQL Server, or MySQL).
- Solid experience in data modeling — relational and dimensional modeling (Star/Snowflake schemas).
- Hands-on experience designing and building ETL/ELT pipelines using Python-based frameworks.
- Working experience with NoSQL databases, particularly MongoDB.
- Hands-on experience with AI Development Tools such as Claude Code, Devin AI, Cursor, Copilot etc.
- Knowledge of data formats including Parquet, Avro, and JSON, and storage platforms such as HDFS or cloud object stores.
- Proficiency with version control (Git) and CI/CD practices for data engineering workflows.
- Familiarity with data orchestration tools such as Apache Airflow or equivalent schedulers.
- Strong analytical, problem-solving, and communication skills with the ability to work across technical and non-technical teams.
- 6+ years of total experience in software or data engineering.
- Hands-on experience specifically in Python-based data engineering, SQL development, ETL design, and data modeling.
Nice to have
- Experience with PySpark and distributed data processing frameworks for large-scale data workloads is a plus.
- Familiarity with Databricks and/or Starburst is a plus.
- Basic working knowledge of Java is a plus.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
We are seeking a skilled and experienced Senior Data Engineer with strong Python expertise to join our Engineering team. The ideal candidate is passionate about building robust, scalable data pipelines and platforms that power analytics, business intelligence, and data-driven decision-making across the enterprise. This role requires deep hands-on experience in Python-based data engineering, SQL development, data modeling, and ETL/ELT design, along with familiarity with both relational and NoSQL databases. Experience with Big Data technologies such as PySpark, and exposure to platforms like Databricks or Starburst, will be considered a strong advantage. The successful candidate will thrive in a collaborative, fast-paced environment and take ownership of end-to-end data solutions.
Responsibilities
Design, develop, and maintain scalable Python-based data pipelines for ingestion, transformation, and distribution of data across enterprise platforms.
Build reusable Python libraries, frameworks, and automation utilities to support data engineering workflows.
Develop and optimize complex SQL queries, stored procedures, and database objects across multiple relational database platforms.
Design and implement ETL/ELT solutions including staging frameworks, incremental processing, data reconciliation, and error handling.
Develop and maintain logical and physical data models to support both operational and analytical use cases.
Work with NoSQL databases (e.g., MongoDB) to design data structures and access patterns suited to non-relational workloads.
Build and maintain data pipelines using PySpark for large-scale distributed data processing where applicable.
Implement data quality, validation, and completeness controls to ensure accuracy and reliability across data products.
Collaborate with data scientists, analysts, software engineers, and business stakeholders to understand requirements and deliver high-quality data solutions.
Contribute to CI/CD pipelines for automated testing, deployment, and version control of data workflows.
Participate in code reviews, troubleshooting, debugging, and production support of data systems.
Support data governance, security standards, and compliance requirements across all data assets.
Qualifications
Strong proficiency in Python for data engineering, including data transformation, orchestration, workflow automation, and utility development.
Expert-level SQL skills across relational databases (PostgreSQL, Oracle, SQL Server, or MySQL).
Solid experience in data modeling — relational and dimensional modeling (Star/Snowflake schemas).
Hands-on experience designing and building ETL/ELT pipelines using Python-based frameworks .
Working experience with NoSQL databases, particularly MongoDB.
Experience with PySpark and distributed data processing frameworks for large-scale data workloads is a plus.
Familiarity with Databricks and/or Starburst is a plus.
Hands-on experience with AI Development Tools such as Claude Code, Devin AI, Cursor, Copilot etc.
Knowledge of data formats including Parquet, Avro, and JSON, and storage platforms such as HDFS or cloud object stores.
Proficiency with version control (Git) and CI/CD practices for data engineering workflows.
Familiarity with data orchestration tools such as Apache Airflow or equivalent schedulers.
Basic working knowledge of Java is a plus.
Strong analytical, problem-solving, and communication skills with the ability to work across technical and non-technical teams.
Experience
6+ years of total experience in software or data engineering.
Hands-on experience specifically in Python-based data engineering, SQL development, ETL design, and data modeling.
Proven experience building and maintaining production-grade data pipelines and platforms at enterprise scale.
Demonstrated experience working with both relational (PostgreSQL, Oracle, SQL Server) and NoSQL (MongoDB) databases in real-world projects.
Experience working in regulated or highly process-oriented environments, preferably within Financial Services.
Education
Bachelor’s or Master’s degree in Computer Science, Software Engineering, or an equivalent technical field.
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Applications Development------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Primary Location Full Time Salary Range:
$120,800.00 - $170,800.00------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
Automated Processing and AI
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This job opening is for an existing job vacancy.
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