Sr Data Engineer (Snowflake, MongoDB & AI Tools) - 100% Remote - W2 Contract

Job Details

Senior Data Engineer – Snowflake & MongoDB

Long Term Contract

100% Remote

Role Summary

We are looking for a Senior Data Engineer to design, build, and operate reliable data pipelines and platforms that turn AI Assistant, and DevOps data (Cursor, Codex, Claude, CoPilot, Devin and its usage, CI/CD Tools, code and Release quality, delivery impact metrics) into actionable intelligence. You will own Snowflake and MongoDB-based data solutions end to end: ingestion, transformation, modelling, monitoring, and production operations.

Key Responsibilities

• Use generative AI coding assistants (such as GitHub Copilot or Cursor) as first-class tools for code scaffolding, optimization, complex transformations, and automated documentation. Validate and own all AI-generated outputs with rigorous testing.
• Design and build scalable ELT/ETL pipelines ingesting data from APIs, databases, and event/log sources (e.g., Cursor, Codex, Claude, CoPilot, Devin, Jenkins, SonarQube, Git, Jira, AI-assistant telemetry) into Snowflake.
• Hands-on experience using dbt to build and maintain data models, write tests, and generate documentation.
• Hands On experience with data visualization libraries or tools such as PowerBI, Tableau
• Implement automated data observability, quality checks, lineage tracking, and compliance frameworks to monitor data drift and maintain trusted data products.
• Design and build scalable ELT/ETL pipelines ingesting data from APIs, databases, and event/log sources (e.g., Cursor, Codex, Claude, CoPilot, Devin, Jenkins, SonarQube, Git, Jira, AI-assistant telemetry) into Snowflake.
• Model data in Snowflake (staging, core, marts; dimensional/star schemas) and optimize performance and cost (clustering, warehouse sizing, query tuning, resource monitors).
• Design and manage MongoDB collections, schemas, indexes, aggregation pipelines, and change streams; integrate MongoDB with the analytics layer.
• Set up monitoring, alerting, and logging for pipelines and databases; lead incident triage, root cause analysis, and runbook creation.
• Automate deployments with CI/CD, Git, and infrastructure-as-code; manage environments (Dev/QA/Prod) and release practices.
• Implement RBAC, masking, encryption, and audit controls aligned with enterprise security and compliance.
• Partner with analysts, BI developers, and product/delivery leads to translate business needs into data products and dashboards.

Required Skills

• 5+ yrs hands-on: Snowflake: architecture, virtual warehouses, Snowpipe, Streams & Tasks, Time Travel, cloning, secure data sharing, semi-structured (VARIANT/JSON) handling, stored procedures, performance and cost optimization.
• 3+ yrs hands-on: MongoDB: data modelling, indexing strategy, aggregation framework, replication/sharding concepts, backup/restore, performance tuning, Atlas or self-managed operations.
• Languages: Advanced SQL and Python (pandas, requests, PySpark a plus); strong scripting (Bash).
• Data integration: ETL/ELT design patterns, batch and incremental/CDC loads, idempotency, error handling and retries. Tools such as dbt, Airflow, Informatica, Talend, Fivetran, or equivalent.
• Data concepts: Dimensional modeling, data warehousing, data lake/lakehouse concepts, data governance, metadata, and lineage.
• Monitoring & operations: Pipeline observability, alerting (e.g., Grafana, Splunk, Datadog, CloudWatch), SLA/SLO management, incident and problem management, on-call/support participation.

Good to Have

• Snowflake Snowpark or Cortex; SnowPro certification.
• MongoDB or Snowflake certification (Associate/Professional DBA or Developer).
• Familiarity with DevOps toolchain data (Jenkins, SonarQube, GitHub/GitLab, Jira) and DORA/engineering metrics and DevOps Practices
• Exposure to AI/ML data preparation or AI-assistant usage analytics.