Senior Data Engineer
RemoteUnited States
Job Summary
Design, optimize, and maintain stored procedures and SSRS reports across the ClubReady reporting environment while owning the customer-facing Postgres data mart product serving enterprise clients. Co-own migrating from legacy Go ETL to the new Fivetran, Snowflake, dbt, and Postgres pipeline, partnering with the central data team. Serve as technical domain expert to assist data mart customers, triage and resolve data discrepancies, and tune queries and indexes against multi-billion-row tables. Author Azure DevOps work items, review pull requests, and operate inside two-week sprints. Join customer calls directly with enterprise account managers and analysts to scope and deliver work.
Required Qualifications
- 5+ years of data engineering or BI development experience with deep T-SQL and SQL Server in a transactional, customer-facing environment
- Bachelor's degree in Computer Science, Information Systems, Engineering, or related field — or equivalent professional experience
- Production experience with at least one analytics warehouse stack (Snowflake, BigQuery, or Redshift) and modern ELT tooling (dbt, Fivetran, Airbyte, or similar)
- Strong query performance instincts: indexing strategy, execution plans, statistics, partitioning, and rewrite patterns for legacy procs
- Proficiency in SQL and at least one scripting language (Python preferred)
- Experience with SSRS or another enterprise reporting platform
- Demonstrated ability to operate as a senior IC: own scope end-to-end without close supervision
- Excellent written and verbal communication
- Comfort working directly with external customers and stakeholders (finance teams, account managers, customer-side analysts)
Desired Qualifications
- Experience supporting a multi-tenant data product (per-customer schemas, row-level security, tenant onboarding workflows)
- Postgres at production scale
- Experience within the boutique fitness industry
- Go experience for maintaining the legacy custom ETL during the sunset period
- Familiarity with Azure DevOps for source control and work tracking
- Exposure to AI-assisted analytics work (Snowflake Cortex, Claude, embedded LLM reporting)
- Track record of mentoring or growing more junior engineers
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