Job
Description
For Data Engineer Years of experience -3-5 years Number of openings-2 For Sr. Data Engineer Years of experience- 6-10 years Number of openings-2 About Us Logic Pursuits provides companies with innovative technology solutions for everyday business problems. Our passion is to help clients become intelligent, information driven organizations, where fact-based decision-making is embedded into daily operations, which leads to better processes and outcomes. Our team combines strategic consulting services with growth-enabling technologies to evaluate risk, manage data, and leverage AI and automated processes more effectively. With deep, big four consulting experience in business transformation and efficient processes, Logic Pursuits is a game-changer in any operations strategy. Job Description We are looking for an experienced and results-driven Data Engineer to join our growing Data Engineering team. The ideal candidate will be proficient in building scalable, high-performance data transformation pipelines using Snowflake and dbt and be able to effectively work in a consulting setup. In this role, you will be instrumental in ingesting, transforming, and delivering high-quality data to enable data-driven decision-making across the clients organization. Key Responsibilities Design and build robust ELT pipelines using dbt on Snowflake, including ingestion from relational databases, APIs, cloud storage, and flat files . Reverse-engineer and optimize SAP Data Services (SAP DS) jobs to support scalable migration to cloud-based data platforms . Implement layered data architectures (e.g., staging, intermediate, mart layers) to enable reliable and reusable data assets. Enhance dbt/Snowflake workflows through performance optimization techniques such as clustering, partitioning, query profiling, and efficient SQL design. Use orchestration tools like Airflow, dbt Cloud, and Control-M to schedule, monitor, and manage data workflows. Apply modular SQL practices, testing, documentation, and Git-based CI/CD workflows for version-controlled, maintainable code. Collaborate with data analysts, scientists, and architects to gather requirements, document solutions, and deliver validated datasets. Contribute to internal knowledge sharing through reusable dbt components and participate in Agile ceremonies to support consulting delivery. Required Qualifications Data Engineering Skills 3–5 years of experience in data engineering, with hands-on experience in Snowflake and basic to intermediate proficiency in dbt. Capable of building and maintaining ELT pipelines using dbt and Snowflake with guidance on architecture and best practices. Understanding of ELT principles and foundational knowledge of data modeling techniques (preferably Kimball/Dimensional) . Intermediate experience with SAP Data Services (SAP DS), including extracting, transforming, and integrating data from legacy systems. Proficient in SQL for data transformation and basic performance tuning in Snowflake (e.g., clustering, partitioning, materializations). Familiar with workflow orchestration tools like dbt Cloud, Airflow, or Control M . Experience using Git for version control and exposure to CI/CD workflows in team environments. Exposure to cloud storage solutions such as Azure Data Lake, AWS S3, or GCS for ingestion and external staging in Snowflake. Working knowledge of Python for basic automation and data manipulation tasks. Understanding of Snowflake's role-based access control (RBAC) , data security features, and general data privacy practices like GDPR. Data Quality & Documentation Familiar with dbt testing and documentation practices (e.g., dbt tests, dbt docs). Awareness of standard data validation and monitoring techniques for reliable pipeline development. Soft Skills & Collaboration Strong problem-solving skills and ability to debug SQL and transformation logic effectively. Able to document work clearly and communicate technical solutions to a cross-functional team. Experience working in Agile settings, participating in sprints, and handling shifting priorities. Comfortable collaborating with analysts, data scientists, and architects across onshore/offshore teams. High attention to detail, proactive attitude, and adaptability in dynamic project environments. Nice to Have Experience working in client-facing or consulting roles. Exposure to AI/ML data pipelines or tools like feature stores and MLflow Familiarity with enterprise-grade data quality tools Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. Certifications such as Snowflake SnowPro, dbt Certified Developer Data Engineering are a plus Additional Information Why Join Us? Opportunity to work on diverse and challenging projects in a consulting environment. Collaborative work culture that values innovation and curiosity. Access to cutting-edge technologies and a focus on professional development. Competitive compensation and benefits package. Be part of a dynamic team delivering impactful data solutions Required Qualification Bachelor of Engineering - Bachelor of Technology (B.E./B.Tech.)
