Note: The job is a remote job and is open to candidates in USA. ASM Tech Solutions is a strategic AI partner to Fortune 500 companies, and they are seeking a Data Engineer / Data Modeler. The role involves building scalable data pipelines and designing data models for business intelligence, analytics, and AI use cases while working directly with clients across various industries.
Responsibilities
- Design and build scalable batch/streaming pipelines to ingest, transform, and deliver data into cloud platforms
- Design conceptual, logical, and physical data models (dimensional, relational, semantic) for BI, analytics, and AI consumption
- Translate business requirements into star/snowflake schemas and ER diagrams with architects and governance teams
- Build and maintain data warehouses/lakehouses on Azure, AWS, GCP, or Snowflake
- Implement data quality, validation, and reconciliation frameworks
- Enforce data modeling standards and documentation practices
- Tune pipeline and query performance for large-scale datasets
- Structure data for ML/GenAI use cases in collaboration with data science teams
- Work across the full SDLC with strong engineering practices (version control, CI/CD, code review)
- Present and defend data model designs to technical and business stakeholders
Skills
- Bachelor's in Computer Science, Engineering, or related field (Master's preferred)
- 4–8 years in data engineering, including 3+ years of hands-on data modeling (relational and dimensional)
- Experience in a consulting or client-facing delivery environment is a plus
- Languages: Advanced SQL (T-SQL/PL-SQL), Python
- Data Modeling: Dimensional modeling, star/snowflake schemas, ER diagramming, data warehouse design
- Cloud: Azure (ADF, Synapse, Databricks) and/or AWS (Glue, Redshift) and/or GCP (BigQuery)
- Big Data: Spark, Kafka
- Platforms: Snowflake, Databricks
- ETL/ELT: ADF, SSIS, dbt
- Engineering: Git, Terraform, Docker, CI/CD
- Strong stakeholder communication and ability to simplify complex models
- Ownership mindset and comfort with ambiguity
- First-principles problem-solving
- Documentation discipline and attention to detail
- Semantic layer design for self-service and AI-ready analytics
- Exposure to feature stores or vector databases for RAG/LLM pipelines
- Data governance/catalog tools (Purview, Collibra, Atlas)
- BI tools (Power BI, Tableau) from a modeling standpoint
- Exposure to Azure OpenAI, AWS Bedrock, or Vertex AI
- MLOps familiarity
- Bash/PowerShell scripting
- API-based/event-driven data integration
- Microsoft Azure Data Engineer Associate (DP-203)
- SnowPro Core/Advanced: Data Engineer
- Databricks Certified Data Engineer
- Google Professional Data Engineer
Company Overview