Job Summary
The Technical Specialist will be responsible for managing Snowflake data platform, using DBT for transformation, and developing SQL and Python scripts to support data operations. They will play a key role in optimizing and maintaining database performance, ensuring data quality, and implementing data pipelines.
Senior Data Engineer – Informatica to dbt MigrationWe are seeking an experienced Senior Data Engineer to support a large-scale data modernization initiative involving the migration of Informatica workflows to dbt on Snowflake. The role will be part of a factory-based delivery model responsible for workflow analysis, code conversion, testing, reconciliation, deployment, and certification.The engineer will analyze Informatica workflows, mappings, sessions, mapplets, reusable transformations, parameter files, source-to-target mappings, and workflow dependencies. The role requires identifying transformation logic, business rules, lookups, joins, filters, aggregations, update strategies, pre/post SQL, scripts, and unsupported patterns that may require redesign.The engineer will convert Informatica mappings into modular and maintainable dbt models using Snowflake SQL. Responsibilities include developing staging, intermediate, and presentation-layer models, reusable macros, incremental models, snapshots, seeds, and dbt tests. The engineer will also review and remediate code generated through automated conversion tools and ensure compliance with project coding, naming, documentation, and architecture standards.A major responsibility will be validating dbt output against the corresponding Informatica output. This includes row-count validation, column-level comparison, aggregate checks, null and duplicate checks, checksum validation, business-rule validation, and reconciliation reporting. The engineer will support parallel runs, investigate data discrepancies, resolve defects, and prepare evidence required for technical and business certification.The role will also support unit testing, system integration testing, regression testing, performance testing, user acceptance testing, production deployment, smoke testing, and post-production validation. The engineer will assist with Git-based code management, pull requests, CI/CD pipelines, deployment documentation, Control-M or equivalent scheduling, rollback planning, and hypercare support.Candidates should have 5–10 years of data engineering or ETL experience with strong hands-on expertise in Informatica PowerCenter, dbt, Snowflake, and advanced SQL. Experience with workflows, mappings, mapplets, parameterization, incremental processing, stored procedures, reconciliation, and ETL modernization is required. Knowledge of Git, CI/CD, Agile delivery, data quality, and performance optimization is also expected.Experience in the insurance domain, factory-based migration programs, Control-M, automated conversion accelerators, and Snowflake or dbt certification is preferred. The candidate should possess strong analytical, troubleshooting, communication, documentation, and stakeholder-management skills.Success will be measured through conversion productivity, first-time-right quality, reconciliation accuracy, low defect leakage, adherence to standards, timely certification, and stable production deployment.
Key Responsibilities
2. Utilize dbt to create and maintain data transformation workflows, ensuring data accuracy and consistency.
3. Develop sql queries and stored procedures to extract and manipulate data for business requirements.
4. Write python scripts for automating data tasks, data processing, and data quality checks.
5. Collaborate with cross functional teams to design and implement efficient data pipelines and etl processes.
6. Troubleshoot and resolve data related issues, ensuring data integrity and reliability.
7. Stay updated with the latest trends and best practices in snowflake, dbt, sql, and python to enhance data operations.
Skill Requirements
2. Proficiency in utilizing dbt for data transformation and modeling.
3. Excellent command of sql for querying and manipulating data in databases.
4. Advanced coding skills in python for scripting, automation, and data processing.
5. Ability to collaborate effectively with cross functional teams to deliver data solutions.
6. Strong analytical and problem-solving skills to troubleshoot data issues and optimize data processes.