Senior Associate - Analytics Engineer

Location:

Hybrid - 3 days per week

The Senior Associate, Analytics Engineer is a practitioner who designs and implements scalable analytics engineering solutions with a high degree of independence. You lead your own work streams 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.

You are technically strong, self-directed, and effective at translating business requirements into well-structured engineering work. You 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. You advise peers on data modeling and analytics engineering best practices, and actively contribute to improving the team’s SDLC practices.

• 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 Metric Flow-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 Big Query 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…