Data Operations Manager

Datavations is a New York-based data and AI platform serving the $2.3 trillion building materials industry. We help manufacturers make smarter decisions about pricing, inventory, and customer relationships by turning massive retail datasets into actionable intelligence. Our platform processes terabyte-scale data from distributors to power competitive intelligence products that drive real revenue impact.

• We value execution: momentum is everything 
• Belief in the power of positivity: we are encouraging and take risks 
• Proactive ownership: we are passionate about driving financial value with creative data product 
• Foster collaboration: we welcome help and always extend a helping hand, internally and externally 

We're hiring a Manager of Data Operations to lead our Data Operations team responsible for ensuring world-class data quality across our platform. You'll report directly to our CTO and own the operational backbone that makes everything else possible - from daily client deliverables to AI model training to executive decision-making.

This isn't a "set it and forget it" role. You'll start hands-on - writing SQL, investigating client data issues, and deeply understanding our data pipeline architecture. As you gain context, you'll evolve into driving strategic improvements: automating manual processes, preventing issues before they reach clients, and building the systems that let us scale without breaking.

The ideal candidate is someone who's been the "go-to problem solver" on their current team - the person who jumps in when things break, who sees process gaps others miss, and who's ready to formalize their leadership with direct reports. You've been carrying more weight than your title suggests. This is your chance to make it official.

• Manage 6-8 data operations analysts across US, Ukraine, and Brazil timezones
• Recruit and scale the team as our client base grows
• Run 1:1s, provide coaching, and handle performance management for a distributed workforce
• Build coverage protocols so critical issues (especially aggregation) get handled immediately, regardless of timezone
• Establish team communication rhythms that feel turnkey - no dropped balls, no confusion about ownership

• Triage incoming data issues with Engineering leads to route problems efficiently
• Jump into firefighting when needed - write the SQL queries, investigate the anomalies, figure out root cause
• Maintain and evolve our centralized data issue tracking system 
• Success = zero data quality issues reaching client hands

• Document current workflows and pain points, then propose fixes
• Build validation frameworks and automated checks that catch problems upstream
• Define and track team KPIs: turnaround times, accuracy metrics, SLA adherence
• Balance reactive work with proactive system building 

• Work with Customer Success and GTM teams on client communications 
• Present operational updates and improvement proposals to CTO and leadership
• Maintain documentation that non-technical stakeholders can understand

• 5-7 years in data operations, analytics, or similar role - you've been in the trenches and understand what "clean data" actually takes
• Proven track record of proactive ownership - you don't wait to be told there's a problem; you hunt for them
• Strong SQL skills - comfortable writing complex queries, debugging data pipelines, investigating anomalies in large datasets
• 1-2 years leading others - you've mentored junior analysts, led projects, or been the person others turn to for guidance
• Clear, concise communicator - equally comfortable explaining technical issues to engineers and business impact to non-technical stakeholders
• Based in Eastern or Central timezone

• Experience managing distributed/remote teams across timezones
• Familiarity with data warehousing tools (ClickHouse, Snowflake, BigQuery, Redshift)
• Exposure to workflow orchestration tools (Prefect, Airflow, dbt)
• Background in data visualization (Hex, Preset, Tableau) for building QA dashboards
• Understanding of statistical accuracy measures (correlation, variance, sampling)

• High-Impact Role: Shape the quality of our data—fueling everything from daily decision-making to innovative AI initiatives.
• Growth & Innovation: Drive process improvements, develop new best practices, and experiment with emerging DataOps techniques.
• Collaborative Team Culture: Partner with skilled professionals in Engineering, Product, and Data Science who share your passion for excellent data.

The base salary average range for this role is $150-170,000 per year, depending on experience, skills, and alignment with the role’s responsibilities. Total compensation may also include equity and a comprehensive benefits package.

We’re committed to paying competitively and equitably, and we regularly review our compensation structures to ensure they align with the market and support our values.

#LI - BB1