Role Description We at Coforge are hiring a Senior Analytics Engineer (#22046) with the following skill set. - Design and maintain scalable analytical data models, data warehouses, and ETL/ELT pipelines to support enterprise reporting and analytics. - Develop interactive dashboards and self-service BI solutions using modern visualization tools while ensuring data quality, consistency, and performance. - Perform complex SQL-based analysis to deliver actionable business insights and support strategic decision-making. - Collaborate with business and technical stakeholders to define KPIs, establish analytics best practices, and drive continuous improvement across the analytics platform. Qualifications - 5+ years of experience in Analytics Engineering, Business Intelligence, or Data Engineering. - Expert-level proficiency in SQL, including query optimization, transformations, and performance tuning. - Strong experience with Data Warehousing concepts, dimensional modeling (Star/Snowflake schemas), and Data Marts. - Hands-on experience building dashboards and reports using Power BI, Tableau, Looker, or similar BI platforms. - Experience designing and maintaining ETL/ELT pipelines. - Working knowledge of PL/SQL. - Experience with Snowflake and modern cloud data platforms. - Familiarity with dbt, Git, and version control best practices. - Experience working with insurance data models, KPIs, and business processes. - Strong analytical, communication, and stakeholder management skills. Requirements - Experience enabling self-service analytics and promoting data literacy. - Knowledge of Agile delivery methodologies. - Experience implementing data governance and data quality frameworks. - Familiarity with cloud-native analytics architectures. - Experience with large-scale enterprise reporting environments. Company Description At Coforge, we hire professionals based solely on their skills and qualifications and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

Role Description Design and build data models and BI dashboards, data visualizations, and applications to solve business problems. Support Analytics Engineering technical solutions and best practices using requirements and direction. Responsibilities - Data Modeling & Visualization: - Understand the basics for modeling and implement best practices for data visualization. - Design performant data models using SQL and BI development tools. - Functional/Technical Requirements: - Collaborate as part of an Agile team with Product Managers, Analysts, Analytics Engineers, and Data Engineers. - Translate technical and business concepts and apply data and BI solutions. - Program/Portfolio Management Support: - Work within an established program management plan to achieve specific goals. - Support and maintain production processes and troubleshoot issues. - Coordinate code review with engineering, data validation, and QA/UAT with analysts and business partners. - Technical Developments Recommendation: - Design, build, and deploy new data models and BI applications and enhance existing in production. - Support efforts and suggest ways to optimize solutions to better meet business, performance, and/or quality needs. - Ongoing Learning and Development: - Develop own capabilities by participating in assessment and development planning activities as well as formal and informal training and coaching. Qualifications - Experience with Business Intelligence (BI) tools (e.g. Microsoft Power BI, QlikSense, Looker, Tableau). - Experience with cloud platforms (e.g. Microsoft Azure, Google Cloud Platform (GCP)). - Experience with cloud data warehouses (e.g. Snowflake, Google BigQuery). - Experience with databases (e.g. Oracle). - Experience with version control systems and CI/CD (e.g. GitHub, GitHub Actions). - Development experience in SQL. - Python development and data architecture experience preferred. - Preferred experience with: - Databricks: Unity Catalog, SQL stored procedures, job orchestration, Databricks Asset Bundles (DABs), Metric Views, Genie. - Power BI: report/dashboard development, DAX measures, data modeling, Power Query. - SQL: advanced patterns including CTEs, MERGE/upsert, window functions, parameterized stored procedures, dimensional modeling. - Python: PySpark, notebook-based ETL workflows. - Retail/merchandising analytics domain knowledge. Education - Bachelor's Degree or Equivalent Level Preferred. Experience - 1-3 Years of Experience. Virtual Requirements - Cameras must be on during all virtual interviews. - AI tools are not permitted to be used by the candidate during any part of the interview process. - Offers are contingent upon a satisfactory background check which may include ID verification.

• Design, build, deploy, and maintain highly resilient and scalable systems that are critical to the success of Data at Toast • Develop and execute technical strategies to solve complex business challenges, leveraging data-driven decisions to prioritize impactful changes. • Build and maintain strong partnerships across lines of business, aligning technical solutions with strategic objectives. • Mentor and guide engineers, fostering a culture of continuous improvement, inclusivity, and engineering excellence. • Effectively communicate technical direction, guide teams through execution and drive organizational quality practices • Diagnose performance bottlenecks and implement optimizations • Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.

• Banking Analytics: Apply domain expertise to identify trends, assess risk, and track core financial metrics. • Data Engineering: Design, build, and optimize ETL pipelines feeding analytical databases. • Visualization Suite: Build interactive, automated dashboards for executive and operational decision-making. • System Migration: Translate and modernize legacy SAP BusinessObjects reports into a self-service PowerBI environment. • Performance Tuning: Write optimized data transformation scripts and data models for large-scale datasets.