Senior Data Analytics Engineer

BlueVoyant•College Park, MD

1d•Remote

About The Position

We are seeking a GTM Data Analytics Engineer to design, build, and scale our analytics platform from the ground up. You will own our Snowflake environment end-to-end, create robust data pipelines from core SaaS tools (Salesforce, Gong, Spiff, Clay, Common Room) and internal product usage data, and enable self-service analytics via Power BI or Tableau. This role partners closely with RevOps, Sales, CS, Product, and Finance, and includes regular communication and presentation to senior leadership.

Requirements

5+ years of data engineering experience, with 3+ years hands-on in Snowflake and a track record of building a Snowflake environment from scratch.
Deep expertise with Snowflake features: virtual warehouses, RBAC, security policies, Time Travel, zero-copy cloning, Snowpipe, Streams, Tasks, materialized views, and performance tuning.
Proven experience building pipelines from Salesforce, Gong, Spiff, Clay, and Common Room, plus internal product usage telemetry (e.g., events, logs). Comfortable with third-party connectors (e.g., Fivetran/Stitch), APIs/webhooks, and Python-based ingestion.
Strong SQL and Python; experience with dbt for transformation and testing, Git-based workflows, and CI/CD.
Advanced data modeling skills (dimensional modeling/Kimball), data warehousing concepts, and building semantic layers for BI.
Extensive experience with Power BI or Tableau, including data modeling (e.g., DAX or Tableau calculations), dashboard design, and performance optimization with Snowflake as the backend.
Excellent communication skills and comfort presenting to senior leadership; ability to translate technical details into business value and actionable insights.
Experience in a major cloud (AWS, Azure, or GCP) including IAM/SSO, networking, secrets management, and logging/monitoring.
Strong focus on data quality, documentation, and operational excellence.

Nice To Haves

Experience with RevOps data, CRM schemas, Gong audio/transcript metadata, compensation/commission data (Spiff), lead enrichment (Clay), and community/product signals (Common Room).
Familiarity with product analytics tools and event pipelines (Segment, Snowplow, Amplitude) and modeling product usage and adoption metrics.
SnowPro Core/Advanced Architect certification or equivalent cloud certifications.
Experience with metric layer tooling, data contracts, and governance frameworks.

Responsibilities

Architect and stand up a Snowflake environment, including account setup, databases and schemas, virtual warehouse strategy, RBAC, security, data governance, cost controls, and monitoring.
Design and implement reliable ELT pipelines from Salesforce, Gong, Spiff, Clay, Common Room, and internal product data sources using connectors, APIs, and Snowflake-native capabilities (Snowpipe, Streams, Tasks).
Ingest and normalize semi-structured data (JSON/VARIANT), implement CDC, and build resilient orchestration with tools such as Airflow, Prefect, Dagster, or cloud-native schedulers.
Model data for analytics using best practices (dimensional modeling/Kimball, data marts, medallion architecture) to create trusted, documented datasets for GTM, Product, and Finance use cases.
Own dbt-based transformations, testing, and CI/CD, including version control, code reviews, and deployment workflows.
Establish data quality, reliability, and lineage standards with automated tests, monitoring, alerting, and SLAs; create runbooks and documentation.
Build and optimize BI assets in Power BI or Tableau, including semantic models, governed metrics, executive-ready dashboards, and query performance optimization against Snowflake.
Partner with stakeholders to define and operationalize key metrics (pipeline, conversion, ARR, retention, product adoption), and regularly present findings and recommendations to leadership.
Implement and maintain security and compliance controls (PII handling, data masking, role-based access, auditing, retention).
Monitor and tune performance and cost (warehouse sizing, caching, micro-partitioning, clustering, materialized views); drive continuous improvement in reliability and efficiency.
Evaluate and integrate new tools and vendors; contribute to data platform roadmap and best practices.