Data Engineer with Snowflake, MongoDB & AI - W2 Contract

Required Skills

Core Platforms

• Snowflake (5+ years hands-on): Architecture, virtual warehouses, Snowpipe, Streams & Tasks, Time Travel, cloning, secure data sharing, semi-structured ( VARIANT /JSON) handling, stored procedures, performance and cost optimization.

• MongoDB (3+ years hands-on): Data modelling, indexing strategy, aggregation framework, replication/sharding concepts, backup/restore, performance tuning, Atlas or self-managed operations.

Technical Capabilities

• 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

• Certifications: SnowPro certification, MongoDB certification (Associate/Professional DBA or Developer).

• Advanced Snowflake Features: Snowflake Snowpark or Cortex.

• Domain Knowledge: Familiarity with DevOps toolchain data (Jenkins, SonarQube, GitHub/GitLab, Jira), DORA/engineering metrics, and DevOps practices.

• AI Analytics: Exposure to AI/ML data preparation or AI-assistant usage analytics.

Qualifications

• Education: Bachelor’s or Master’s in Computer Science, Engineering, or a related field (or equivalent experience).

• Experience: 7+ years in data engineering with demonstrated production experience on Snowflake and MongoDB.