Senior Data Engineer

Senior Data Engineer

About Our Company

Ameriprise India LLP has been providing client based financial solutions to help clients plan and achieve their financial objectives for 20 years. We are part of Ameriprise Financial Inc., a US financial planning company headquartered in Minneapolis with a global presence and diversified financial services leader with more than $1.5 trillion in assets under management, administration and advisement as of year-end 2024. The firm’s focus areas include Asset Management and Advice, Retirement Planning and Insurance Protection.

Be part of an inclusive, collaborative culture that rewards you for your contributions, and work with other talented individuals who share your passion for doing great work. You’ll also have plenty of opportunities to make your mark at the office and a difference in your community. So, if you're talented, driven and want to work for a strong, ethical company that cares, take the next step and create a career at Ameriprise India LLP.

Job Description

This role is accountable for recurring analytics deliverables and controls that support frequent reporting, model output validation, and ad hoc analysis to test attributes for future model inclusion. The position ensures data quality, designs and maintains analytics-ready data models and production data pipelines, supports model development and maintenance, and communicates with senior leaders on report questions. The role should translate business requirements into governed, reusable datasets, metric definitions, and scalable reporting assets.

Key Responsibilities

• Reporting, Analytics & Data Product Ownership: Own end-to-end data pipelines to facilitate Predictive Analytics workflows. This includes data that supports monthly pipeline dashboard validation, trend and offer-lag analysis, quarterly attrition reporting, and EAR CBA performance tracking; ensure data accuracy through strong controls, consistent definitions, version management, and partnership with data/automation teams to transition manual reports to scalable automated solutions. Treat recurring datasets, metrics, and dashboards as governed data products with clear ownership, lineage, documentation, and support expectations.
• Stakeholder management and proactive communication: Collaborate with business leaders, technology support, data engineering, BI, and modeling SMEs and maintain connectivity to Finance data initiatives to align assumptions and sourcing. Translate business requirements into source-to-target logic, data model requirements, acceptance criteria, and clear handoffs for technical partners.
• Analytics engineering practices: Apply software engineering discipline to analytics workflows, including modular SQL/Python transformation logic, peer review, version control, testing, deployment readiness, lineage awareness, and clear documentation of business rules.

• Operational readiness & continuity: Build and maintain process documentation/procedures and ensure access continuity through co-ownership and trained backups. Own runbooks for recurring pipelines and reporting assets, including upstream/downstream dependencies, failure points, validation checks, escalation paths, and release/change management steps.

Required Qualifications

•  3–6 years of experience in data engineering, analytics engineering, reporting, dashboarding, business intelligence, risk analytics, or model operations within financial services or a data-driven environment. Experience should include building or maintaining reusable datasets, reporting layers, or productionized analytics workflows—not only performing ad hoc analysis.

• Strong experience with data validation, reconciliations, and control routines for recurring reporting and/or model outputs.

• Proficiency in SQL and Python; experience with data visualization/reporting tools (e.g., Power BI).
• Working knowledge of modern data stack concepts, including data warehouses/lakehouses, ELT patterns, dimensional modeling, semantic layers, data lineage, testing, and version-controlled analytics code.
• Ability to partner with data engineering teams on ingestion, orchestration, access patterns, and production support while owning the analytics transformation and business logic layer.

• Working knowledge of Project Management concepts and the ability to partner effectively & drive the project/analytics
• Experience managing recurring deliverables with clear timelines, stakeholder communications, and documentation standards.
• Strong written communication skills to translate analytical findings into concise, decision-ready narratives.

Preferred Qualifications

•  Experience automating data workflows (using technologies like Python, SQL, AWS and Snowflake) and validating automated outputs after transition. Exposure to transformation frameworks or similar patterns (e.g., dbt-style modular models, scheduled jobs, reusable SQL layers, automated data tests) is strongly preferred.
• Experience designing analytics data models such as fact/dimension tables, curated reporting tables, certified datasets, or semantic model inputs for Power BI or similar BI tools.
• Experience using Jira (or similar) to manage analytics/project work and support team execution rhythms.

• Domain exposure to pipeline/CRM data, sales performance analytics, advisor analytics, collections analytics, or attrition/churn modeling.
• Bachelor’s degree Computer Science discipline or other quantitative field (Math, Stats, Data Science); advanced degree a plus.

Core competencies

• Analytical rigor and attention to detail (data quality, validation, reconciliations)

• Data modeling and analytics engineering mindset: reusable transformations, governed metrics, lineage, testing, and maintainability

• End-to-end ownership of recurring deliverables (planning, execution, review, distribution)
• Stakeholder management and proactive communication

• Process improvement, automation, and production-quality analytics engineering mindset

• Documentation discipline and operational risk awareness
• Ability to work independently while coordinating across model owners, technology, and business partners

Measure of success

• On-time delivery of monthly/quarterly reporting and governance artifacts with minimal rework.
• Reduced defects and faster issue identification through strong validation controls and clear escalation paths.
• Successful migration of targeted manual reports to automated processes with stable post-launch performance.

• Reusable, documented analytics datasets and metric definitions reduce duplicated logic across dashboards, recurring reports, and model support analyses.
• Pipeline and reporting changes are traceable, tested, and production-ready before handoff or release.

• Improved stakeholder understanding via concise, high-signal trend narratives and actionable insights.
• Documented, repeatable processes with backup coverage and resilient access/distribution mechanisms.

In-Office Collaboration

We are a client-centric, relationship-based business. Working together, in-person, is foundational to how we achieve results. By fostering a culture of face-to-face collaboration, idea sharing, productivity and personal connection, we deliver for our stakeholders — clients, advisors, employees and shareholders. Our employees work in the office at least three (3) days per week, with flexibility to work from home two (2) days per week. Some roles may require additional in-office time or different in-office expectations, and specific requirements will be discussed during the hiring process.

Full time(2:00p-10:30p)AWMPO AWMP&S President's OfficeBusiness Support & Operations

Ameriprise India LLP is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, genetic information, age, sexual orientation, gender identity, disability, military status, veteran status, marital status, pregnancy, family status or any other basis prohibited by law.

We are committed to fostering an inclusive and accessible recruitment process for individuals with disabilities. If you require a reasonable accommodation to participate in the application or interview process, speak to your recruiter to discuss how we can support you.

Experience Level

Senior Level