Senior Associate - Analytics Engineer

New York Life•New York, NY

2d•$124,000 - $177,000•Hybrid

About The Position

Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life's businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes. Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era - all backed by the stability and purpose of a mutual company built to last. The Senior Associate, Analytics Engineer is a practitioner who designs and implements scalable analytics engineering solutions with a high degree of independence. They lead their own workstreams end-to-end — from source alignment and data modeling through testing, deployment, and quality monitoring — engaging with the Analytics Engineering Lead and senior engineers for input on the most complex architectural decisions. They are technically strong, self-directed, and effective at translating business requirements into well-structured engineering work. They exercise independent judgment in selecting approaches and techniques, engage directly with business stakeholders to understand requirements, and provide input into team-level goals and delivery planning. They advise peers on data modeling and analytics engineering best practices, and actively contribute to improving the team’s SDLC practices.

Requirements

4+ years of progressive data engineering experience with a strong focus on analytics engineering and transformation pipeline development in production cloud environments.
Strong command of advanced SQL — window functions, CTEs, performance optimization, and complex multi-source joins — and solid Python proficiency for data engineering tasks.
Hands-on dbt experience: layered modeling, incremental models, source definitions, singular and generic tests, macros, and multi-environment project configuration.
Experience with cloud data platforms — Databricks and/or BigQuery required; experience with Snowflake or Redshift a plus.
Understanding of ELT patterns, dimensional modeling, and the design of scalable analytical data products with clear grain, ownership, and consumer contracts.
Proficiency with Git and collaborative development workflows: branching strategy, PR review, and CI/CD pipeline integration.
Experience working in Agile/Scrum delivery environments with structured sprint planning, backlog grooming, and milestone tracking.
Clear written and verbal communication skills — able to explain technical decisions, document data products, and engage effectively with both engineering and business stakeholders.

Nice To Haves

Exposure to semantic layer tooling — dbt Semantic Layer / MetricFlow, Looker LookML, or equivalent — and experience building metric definitions consumed by BI or AI systems.
Familiarity with agentic AI concepts and interest in applying LLM-assisted tooling to pipeline construction, data quality remediation, or transformation pattern generation.
Experience with data observability tooling (Monte Carlo, Anomalo, or dbt built-in monitoring patterns) and building self-monitoring pipeline patterns.
Exposure to graph data models, knowledge graphs, or context graph construction for AI or analytics use cases.
Experience with data catalog and lineage platforms (Dataplex, DataHub, Alation) and column-level governance tagging practices.
Insurance or financial services industry experience, with familiarity with data privacy standards and enterprise compliance requirements.

Responsibilities

Lead, with support from the Analytics Engineering Lead, the design and implementation of scalable dbt transformation pipelines across Databricks, Postgres and Bigquery — covering layered modeling (staging / intermediate / mart), incremental strategies, and source contract definitions.
Design and build well-tested, documented data products — dimensional models, aggregates, and feature tables.
Develop solutions to complex data transformation problems using advanced SQL and Python, selecting the right approach based on evaluation, judgment, and the performance and maintainability requirements of the platform.
Optimize and tune transformation pipelines for performance, cost efficiency, and incremental processing at scale — independently identifying bottlenecks and driving improvements.
Own your data products end-to-end: source alignment, modeling, testing, documentation, deployment, and post-release monitoring, with awareness of downstream BI and AI/ML dependencies.
Lead, with support from senior engineers, the availability, usability, integrity, and security of data within your domain — ensuring data is consistent, trustworthy, and governed in accordance with enterprise standards.
Implement robust dbt test frameworks, source freshness checks, and data quality monitoring patterns that make pipeline health observable and failures diagnosable.
Apply governance standards at the analytics layer: column-level PII tagging, access control integration, and lineage documentation that supports the enterprise data catalog.
Lead efforts to improve SDLC practices within the team — contributing to and helping establish CI/CD pipelines, automated testing, branching conventions, and PR review standards.
Maintain data catalog entries for all owned assets: lineage, ownership, grain documentation, and business glossary alignment.
Develop and maintain reusable macro libraries and dbt modeling patterns that enforce consistency and accelerate delivery across the analytics engineering surface.
Participate in semantic layer development — building MetricFlow-based metric definitions that provide a governed, authoritative source of business logic decoupled from downstream consumption.
Contribute to self-healing pipeline patterns and agentic pipeline construction approaches — prototyping and implementing automated anomaly detection, quality remediation, and LLM-assisted transformation generation.
Support context graph construction that captures relationships between business entities and data assets, enabling richer AI reasoning and cross-domain signal integration.
Stay current with the dbt ecosystem, Databricks and BigQuery platform releases, and the broader analytics engineering field — bringing concrete, evaluated recommendations back to the team.
Engage directly with business stakeholders, data scientists, and ML engineers to understand data requirements — translating them into well-scoped Jira stories with clear acceptance criteria, grain definitions, and delivery estimates.
Partner with Integration Services on ingestion design to ensure source data arrives in shapes that are transformation-ready, correctly typed, and well-governed before reaching the analytics layer.
Collaborate with data stewards across NYL to resolve data quality issues at the source — driving shared accountability for data integrity rather than working around upstream problems.
Communicate technical decisions, modeling trade-offs, and delivery status clearly to the Analytics Engineering Lead and cross-functional partners, adapting style and depth for technical and non-technical audiences.
Advise junior engineers on data modeling approaches, dbt patterns, SQL craft, and analytics engineering best practices through code review and pair-modeling sessions.