Note: The job is a remote job and is open to candidates in USA. Modus Create is seeking a Data Engineer embedded in its Data & ML practice to work as a forward-deployed engineer on enterprise data transformation initiatives. The role focuses on client-facing pre-sales engagements, end-to-end data platform architecture, scalable big data pipelines, AI and ML integration, and reusable engineering solutions.
Responsibilities
- Lead technical pre-sales engagements — Executive Conversations, workshops, POCs — directly alongside Sellers, turning ambiguous client data challenges into validated, production-ready architectural solutions
- Architect and deliver end-to-end data platform solutions across the modern data stack: ingestion, transformation, storage, orchestration, and visualization
- Design and implement scalable big data pipelines using Spark, Scala, EMR, Glue, and Airflow — processing petabyte-scale workloads with reliability and efficiency
- Build and optimize data warehousing and lakehouse solutions on Snowflake, Databricks, Redshift, and Apache Hadoop ecosystems
- Implement data transformation frameworks using dbt and deliver BI solutions across Power BI, Tableau, QuickSight, and Looker
- Integrate AI and ML capabilities into data platforms — building feature stores, ML pipelines, model serving infrastructure, and GenAI-powered data workflows
- Identify repeatable patterns, build reusable architectural IP, and influence senior stakeholders as a credible voice on data modernization strategy
- Drive force-multiplication: create mechanisms, enablement assets, and documentation that scale your impact well beyond your direct engagements
Skills
- Cloud data platforms: hands-on experience with Snowflake, Databricks, Amazon Redshift, and/or Apache Hadoop (HDFS, YARN, Hive) — plus AWS big data services including EMR, Glue, Athena, Lake Formation, and Kinesis
- Big data processing: production Spark in PySpark and/or Scala for batch and streaming; Apache Airflow for orchestration; AWS Glue for serverless ETL/ELT; Kafka or Kinesis for streaming pipelines
- Data transformation & modeling: hands-on dbt (modular pipelines, tests, CI/CD integration); strong SQL across analytical databases; experience with Kimball, Data Vault, or OBT modeling patterns and lakehouse formats (Delta Lake, Iceberg, Hudi)
- Data visualization & BI: experience delivering solutions on one or more of Power BI, Tableau, Amazon QuickSight, or Looker — including semantic layer design, governance, and performance optimization
- AI & ML integration: experience with ML workflows (feature engineering, model pipelines, MLOps with MLflow or W&B); familiarity with GenAI/LLMs in data contexts (RAG, NL2SQL, LLM-powered data quality); exposure to AWS AI services (SageMaker, Bedrock)
- Core engineering: strong Python and/or Scala fundamentals; ability to write clean, tested, maintainable code and apply engineering discipline to data work
- Client-facing delivery: comfort translating technical depth into business language; ability to lead POC engagements end-to-end and present to senior stakeholders
- Distributed teamwork: experience in async-first, globally distributed environments — you don't need to be in the same timezone to drive outcomes
- AWS certifications: Data Analytics Specialty, Solutions Architect, Machine Learning Specialty, or Database Specialty
- Databricks certifications or Snowflake SnowPro credentials
- Experience with data mesh, data contracts, or data product frameworks
- Background in consulting, professional services, or solutions engineering — you know how to run a room
- Familiarity with data governance and cataloging tools (Glue Catalog, Unity Catalog, Collibra)
- Contributions to open-source data tooling, dbt packages, Spark libraries, or reusable architectural patterns
Benefits
- The autonomy to define solutions, the support to deliver them, and the recognition when they land
Company Overview