Role Description We at Coforge are hiring a MarkTech Engineer with the following skill set. Qualifications - Experience in Data Engineering, Analytics Engineering, or MarkTech roles. - Strong hands-on experience with: - Segment (or similar CDP) - Braze (or similar customer engagement platform) - SQL (advanced level) - Experience with identity resolution and user stitching across anonymous and known user states. - Experience with webhook-based data architectures (ingestion, transformation, activation). - Experience integrating data between: - Data warehouses (BigQuery, Redshift) - Marketing platforms (Braze, Drip, Shopify) - Solid understanding of: - Event tracking and schemas - Customer data models (users, sessions, events) - Data pipelines and ETL/ELT concepts Requirements - Experience with Shopify integrations (order events, subscription lifecycle, customer data). - Experience migrating between ESPs (e.g., Drip → Braze transition). - Experience with Braze CDI (Cloud Data Ingestion) and real-time vs batch activation tradeoffs. - Exposure to dbt and modern data stack. - Familiarity with product analytics and growth frameworks (PLG funnels, lifecycle segmentation). - Experience with Fivetran or similar connector platforms. - Comfort with AI-assisted development (Claude, Copilot). - Experience in high-scale, product-led businesses. Company Description At Coforge, we hire professionals based solely on their skills and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

Role Description We are looking for a reliable and detail-oriented Data Entry Specialist to join our operations team. The ideal candidate will be responsible for accurately inputting, updating, and maintaining data across company systems and spreadsheets. This role requires advanced Microsoft Excel skills and a strong commitment to data accuracy and confidentiality. - Accurately enter and update data into company databases, spreadsheets, and internal systems. - Build and maintain advanced Excel spreadsheets including formulas, pivot tables, and data validation. - Review and verify data for errors, inconsistencies, or missing information and make corrections as needed. - Organize and maintain physical and digital records in accordance with company procedures. - Generate routine and ad hoc reports from existing data sets for internal use. - Coordinate with team members to ensure timely and accurate data submissions. - Always maintain confidentiality of sensitive company and client information. - Assist with data audits and cleanup projects as assigned. Qualifications - Minimum 3 years of experience in data entry or administrative support role. - Advanced proficiency in Microsoft Excel (pivot tables, VLOOKUP/XLOOKUP, data validation, conditional formatting, macros). - High school diploma or equivalent required; associate or bachelor’s degree preferred. - Typing speed of at least 50 WPM with high accuracy. - Strong attention to detail and commitment to data integrity. - Familiarity with database software or CRM systems is a plus. - Ability to work independently, prioritize tasks, and meet deadlines. - Strong organizational and time management skills. Requirements - Fully remote - Fluent in English - Will work 8:00 AM to 5:00 PM US Eastern Time (EST)

• Support the development of scalable data pipelines, analytics solutions, and reporting tools. • Develop and maintain analytics-ready datasets, transformations, and data models within Databricks and Azure-based data platforms. • Build and support data transformation workflows, notebooks, and analytics processes utilizing Databricks within the Azure cloud environment. • Support development and optimization of Power BI and Tableau dashboards, datasets, and reporting solutions. • Collaborate with senior engineers to support Azure-based analytics architecture, data pipelines, integrations, and reporting environments. • Support pipelines and transformation logic to prepare high quality, analytics ready datasets. • Provide training, support, and best practice guidance to promote the adoption of self-service analytics tools across the organization.

• Designing and developing scalable and efficient data pipelines, ETL processes, and data integration solutions. • Ensuring data quality and reliability by implementing data validation, cleansing, and quality monitoring processes. • Optimising database performance by tuning queries, implementing indexing strategies, and analyzing performance metrics. • Collaborating with cross-functional teams to gather requirements, understand data needs, and develop solutions. • Staying up-to-date with emerging technologies and industry trends in data engineering. • Establishing and enforcing best practices and standards for data engineering.