Analytics Engineer Jobs

About The Position

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 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 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 the availability, usability, integrity, and security of data within their 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.

Benefits

• leave programs
• adoption assistance
• student loan repayment programs