Mid-to-Senior Data Consultant

About the Role

We are seeking a Mid-to-Senior Data Consultant with proven, practical experience designing and engineering modern data architecture. In this role, you will work directly with enterprise clients to translate complex business requirements into scalable data systems, automated pipelines, and actionable analytics.

If you thrive on hands-on technical execution, cross-functional collaboration, and solving high-impact data challenges across leading cloud platforms, we’d love to hear from you.

What You Will Do

• Build Modern Data Pipelines: Architect, code, and deploy end-to-end ETL/ELT pipelines and automated data workflows.
• Optimize Cloud Storage & Warehouses: Fine-tune database performance, data lakes, and lakehouses across major cloud environments.
• Consult & Advise Enterprise Clients: Evaluate existing client data infrastructure and deliver strategic, high-value recommendations aligned with business goals.
• Collaborate Across Teams: Partner with distributed, cross-functional engineering and business teams to deliver scalable analytics solutions.
• Ensure Data Integrity & Security: Implement robust data governance, security, and quality controls across every layer of the data lifecycle.

What We Are Looking For

• Experience: 4+ years of hands-on experience in production environments as a Data Consultant, Data Engineer, or Data Architect.
• Platform Expertise: Demonstrated success delivering enterprise data solutions on Databricks or Snowflake.
• Technical Proficiency: Advanced SQL and strong proficiency in Python (or Scala).
• Cloud Infrastructure: Direct, hands-on experience with AWS or GCP.
• Communication & Collaboration: Excellent written and spoken English, with a track record of driving impact in fully remote, distributed teams.

Nice to Have

• Certifications: Official certifications in Databricks, Snowflake, AWS, or GCP.
• Modern Tooling: Experience with modern orchestration and transformation tools (e.g., dbt, Airflow, Dagster).
• Data DevOps: Familiarity with CI/CD practices applied to data engineering.