Job Summary
Synechron is seeking a Data Engineer with 5+ years of experience in building data marts, ETL pipelines, and large-scale data-processing solutions.
The role will focus on designing, developing, optimizing, and supporting scalable data pipelines using Python, PySpark, Spark, SQL, and data-warehousing technologies. The position will cover the end-to-end software development lifecycle, including development, testing, UAT support, production deployment, issue resolution, and post-production support.
The role will contribute to business objectives by delivering reliable and efficient data solutions, improving data quality and accessibility, supporting informed decision-making, and enabling consistent data operations across enterprise environments.
Software Requirements
Required Software Skills
Python: Strong hands-on experience developing data-processing applications, ETL workflows, automation scripts, and production services.
PySpark and Spark: Strong experience building distributed data-processing and ETL solutions.
Pandas: Experience with data preparation, analysis, transformation, validation, and profiling.
SQL: Strong experience with data extraction, transformation, validation, analysis, and query optimization.
Oracle SQL: Strong knowledge of Oracle SQL, complex queries, joins, aggregations, subqueries, stored procedures, and performance optimization.
ETL development: Experience designing, developing, testing, deploying, and supporting ETL and data-pipeline solutions.
Data warehousing: Strong understanding of data-warehouse concepts, dimensional modelling, data marts, transformations, fact tables, dimensions, and data quality.
Hadoop, Hive, and MapReduce: Experience working with distributed data-processing and big-data technologies.
SQL and NoSQL databases: Experience working with structured, semi-structured, and unstructured data.
Jupyter Notebook: Experience using notebooks for data analysis, development, validation, troubleshooting, and technical exploration.
CI/CD pipelines: Familiarity with automated build, testing, deployment, and release processes.
Testing and data validation: Experience validating data transformations, pipeline results, data quality, and application behavior.
Software engineering practices: Understanding of coding standards, version control, documentation, code reviews, testing, and maintainable development practices.
Preferred Software Skills
Experience with banking or financial-services data and reporting requirements.
Exposure to cloud-based data platforms and data-processing services.
Familiarity with workflow scheduling, orchestration, monitoring, and alerting tools.
Experience with automated data-quality checks, reconciliation, lineage, and metadata management.
Exposure to infrastructure automation, containerization, and modern DevOps practices.
Familiarity with tools supporting data pipeline observability and performance analysis.
Version and Experience Guidance
Specific software versions are not mandated in the requirements.
Candidates should have experience with current, production-supported versions of Python, PySpark, Spark, Pandas, Oracle SQL, Hadoop, Hive, MapReduce, and related tools.
Candidates should be able to assess compatibility, manage dependencies, support upgrades, and follow version-control practices.
Experience should include production data engineering, pipeline support, and end-to-end SDLC participation rather than only academic or prototype development.
Overall Responsibilities
Design, develop, maintain, and optimize scalable ETL and data-pipeline solutions using Python and PySpark.
Build and enhance data marts and data-transformation processes.
Develop distributed data-processing solutions using Spark, Hadoop, Hive, and MapReduce.
Work with structured, semi-structured, and unstructured data from multiple sources.
Develop and optimize Oracle SQL queries for data extraction, transformation, analysis, and validation.
Analyze and debug data issues using Oracle SQL,…