• Designs, models, documents, and governs the logical and conceptual data architectures, relationships, and schema evolutions for complex, enterprise-scale applications in support of tracking and accountability reforms. • Analyzes current and emerging system requirements, then develops comprehensive technical, structural, and organizational specifications to enable auditable, transparent data flows. • Establishes and enforces data modeling standards, capacity planning, and performance optimization to support high-reliability DoW environments. • Develops robust Python-based scripts and ETL/ELT pipelines for efficient, secure data ingestion, transformation, and integration from diverse sources. • Leverages deep expertise in Relational Databases (SQL) for advanced querying, schema design, optimization, and data integrity assurance. • Operates within Agile/SAFe development frameworks to deliver iterative, high-value increments aligned with business priorities and DoW policies. • Provides technical guidance and mentorship to align development efforts with strategic FMS reform objectives, including enhanced tracking, oversight, and reporting. • Performs additional duties as assigned or required to advance mission goals.

• Implements and advances CHI's data, evaluation, and impact initiatives. • Compiles, cleans, merges, harmonizes, and maintains data from multiple internal and external sources. • Conducts descriptive statistical analyses and applies or adapts consistent methods across projects, learning new methods as needed. • Prepares analytic outputs including tables, visualizations, and narrative summaries. • Establishes and advances quality assurance (QA) and data validation protocols and audits. • Documents and maintains operational procedures, to support accuracy, consistency, usability, and ethical data practices. • Advances data practices that reflect CHI's commitment to equity, including culturally responsive and participatory approaches where appropriate and feasible. • Develop and tests data collection tools to ensure data gathered across the organization is consistent, high-quality, and aligned with organizational goals. • Collaborates with cross-functional teams to communicate findings, translate data into actionable insights, and support evidence-informed decision-making. • Partners with CHI teams to ensure integrated, high-quality data collection, analytics, and insights. • Translates project scopes, analysis plans, and data findings for technical and non-technical internal and external audiences. • Promotes data literacy and capacity across teams. • Develops and refines KPIs, performance metrics, and monitoring tools and processes that drive organizational learning, accountability, and strategic decision-making. • Facilitates proposal development and reporting for general and data- and evaluation-related funding opportunities. • Leads and enhances data-related processes and functions that promote continuous improvement, knowledge sharing, innovation, and alignment with CHI's strategic goals. • Collects and compiles data from public health datasets and research and conducts literature reviews to support CHI's programs, evaluation efforts, and strategic initiatives. • Develops content for and contributes to external publications, presentations, briefs, and other materials that communicate CHI's impact, outcomes, and value proposition. • Ensures the timely fulfillment of data and evaluation-related components of grant, partnership, and contractual deliverables. • Maintains regular communication with project team members and coordinates project activities to support successful execution and delivery.

• Build and scale data pipelines: Design and develop scalable data pipelines, from scoping through delivery • Collaborate cross-functionally: Partner with stakeholders across the business to define data requirements and translate them into technical solutions • Build reliable reporting infrastructure: Create performant, well-tested data models that power internal reporting, self-serve analytics, and decision-making • Design scalable data models: Apply modelling frameworks (one big table, entity tables, event streams, Kimball) tailored to the use case • Optimize and monitor: Maintain pipeline health, optimize query performance, and implement data quality monitoring • Enable the team: Document solutions, establish best practices, and help upskill fellow Analytics Engineers and Analysts • Integrate cross-functional data: Work across domain boundaries to unify data into cohesive, reusable reporting structures

Role Description We at Coforge are hiring an Analytics Engineer with the following skill set. - Transform raw data into trusted, business-ready datasets used by teams such as Sales, Marketing, Product, Engineering, Finance, and People. - Design, build, and maintain scalable analytical models using SQL and dbt. - Translate business requirements and definitions into clear technical data models. - Partner with analysts and business stakeholders to understand reporting, analytics, and operational data needs. - Work with Data Engineers to define source requirements, resolve data issues, and improve the usability of ingested data. - Consolidate duplicated business logic into shared and reusable data models. - Build automated tests and monitoring to improve data quality, accuracy, and reliability. - Document datasets, business definitions, assumptions, ownership, and expected usage. - Support the secure and appropriate handling of sensitive and personally identifiable information. - Review code and contribute to standards for SQL style, testing, documentation, model design, and maintainability. - Help improve the developer experience and delivery processes for analytics code. - Investigate data inconsistencies and collaborate with the appropriate teams to resolve their underlying causes. - Contribute to the evolution of our semantic layer, data governance, and enterprise data modeling practices. Qualifications - Experience in analytics engineering, data analytics, business intelligence, data engineering, or a related role. - Strong SQL skills and experience building production analytical datasets. - Experience translating business requirements into data models, metrics, or reporting solutions. - Experience with dimensional modeling concepts such as fact and dimension tables, star schemas, data grain, and slowly changing dimensions. - Experience working with a cloud data warehouse such as Snowflake, BigQuery, or Redshift. - Experience with dbt or a similar SQL-based transformation framework. - Familiarity with Git, pull requests, code review, and version-controlled development workflows. - Experience with at least one business intelligence or visualization platform, such as Sigma, Tableau, Looker, Power BI, or Metabase. - Understanding of data quality practices, including testing, validation, monitoring, and reconciliation. - Ability to communicate effectively and work collaboratively with technical and business partners. Requirements - Experience with Snowflake, dbt, Airflow, and Python. - Experience working with event-based, transactional, or operational datasets. - Experience building or maintaining semantic layers, shared metrics, or governed analytical datasets. - Familiarity with modern data ingestion tools such as Fivetran, Estuary, or similar platforms. - Experience handling sensitive data, access controls, masking policies, or data governance requirements. - Experience improving analytics development standards, CI/CD workflows, documentation, or data observability. - Familiarity with tools such as Atlan, Sigma, Tableau, or similar data catalog and analytics platforms. - Experience working in a complex organization with multiple source systems, business domains, and stakeholder groups. 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.