Job description

About Us:

Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting-edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.

Our offerings enable clients to streamline and future-proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether it’s solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.

Job1: Summary

We are looking for a hands-on Data Engineer with strong experience in Python, SQL, and data processing frameworks such as PySpark. The role will involve working with large and complex datasets, extracting and transforming raw data, performing data manipulation and deriving meaningful datasets for downstream analytics, reporting, and business use cases.

Key Responsibilities:

Extract, process, clean, transform, and integrate data from multiple structured and unstructured sources.
Develop and maintain data processing and ETL/ELT pipelines using Python, SQL, and PySpark.
Work with large datasets to perform joins, aggregations, transformations, filtering, and data enrichment.
Derive new datasets, attributes, metrics, and data points from raw source data based on business and analytical requirements.
Write optimized SQL queries for data extraction, transformation, and analysis.
Develop scalable data processing solutions using PySpark/Spark where required.
Perform data quality checks, validation, reconciliation, and troubleshooting of data issues.

Job 2 (US Shift) Summary:

We are seeking a highly skilled Senior Data Engineer to build and manage scalable data solutions that power analytics, reporting, and enterprise applications. The role involves working on large-scale data platforms, optimizing database performance, and developing robust ETL pipelines to support business-critical operations. The ideal candidate should have strong expertise in SQL, data modeling, ETL development, and production support, along with experience handling high-volume enterprise datasets.

Key Responsibilities:

Design and develop scalable data pipelines and database solutions.
Build and maintain ETL/ELT workflows for integrating data from multiple sources.
Optimize SQL queries, database performance, and data processing frameworks.
Develop and manage data models, schemas, and backend data structures.
Ensure data quality through validation, reconciliation, and governance processes.
Monitor production systems, troubleshoot issues, and provide operational support.
Collaborate with cross-functional teams to understand business and reporting requirements.
Support analytics and reporting platforms with reliable and high-quality datasets.
Implement best practices for scalability, performance, and reliability of enterprise data systems.

Key Requirements:

Strong expertise in SQL and PL/SQL.
Hands-on experience with relational databases such as Oracle or SQL Server.
Experience in ETL/ELT pipeline development and data integration.
Strong understanding of database design and data modeling concepts.
Experience with query optimization and performance tuning.
Knowledge of data validation, governance, and quality processes.
Exposure to production support and incident management activities.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

About Us:

Job1: Summary

Key Responsibilities:

Extract, process, clean, transform, and integrate data from multiple structured and unstructured sources.
Develop and maintain data processing and ETL/ELT pipelines using Python, SQL, and PySpark.
Work with large datasets to perform joins, aggregations, transformations, filtering, and data enrichment.
Derive new datasets, attributes, metrics, and data points from raw source data based on business and analytical requirements.
Write optimized SQL queries for data extraction, transformation, and analysis.
Develop scalable data processing solutions using PySpark/Spark where required.
Perform data quality checks, validation, reconciliation, and troubleshooting of data issues.

Job 2 (US Shift) Summary:

Key Responsibilities:

Design and develop scalable data pipelines and database solutions.
Build and maintain ETL/ELT workflows for integrating data from multiple sources.
Optimize SQL queries, database performance, and data processing frameworks.
Develop and manage data models, schemas, and backend data structures.
Ensure data quality through validation, reconciliation, and governance processes.
Monitor production systems, troubleshoot issues, and provide operational support.
Collaborate with cross-functional teams to understand business and reporting requirements.
Support analytics and reporting platforms with reliable and high-quality datasets.
Implement best practices for scalability, performance, and reliability of enterprise data systems.

Key Requirements:

Strong expertise in SQL and PL/SQL.
Hands-on experience with relational databases such as Oracle or SQL Server.
Experience in ETL/ELT pipeline development and data integration.
Strong understanding of database design and data modeling concepts.
Experience with query optimization and performance tuning.
Knowledge of data validation, governance, and quality processes.
Exposure to production support and incident management activities.