As a Data Engineer, you will design and implement end-to-end data systems that power intelligent applications in sectors such as Financial Services, Manufacturing, and Energy & Utilities. This role centers on building reliable, scalable data pipelines that support machine learning models and analytics platforms, ensuring data is accurate, accessible, and actionable.
What You’ll Do
- Develop and maintain batch and streaming data pipelines from ingestion through transformation to delivery
- Use dbt to create modular, testable SQL transformation layers with strong documentation and CI/CD integration
- Architect and manage lakehouse environments using open table formats like Delta Lake and Apache Ice游戏副本 on platforms including Snowflake and Databricks
- Orchestrate workflows using tools such as Apache Airflow, Dagster, or Microsoft Fabric
- Implement data quality frameworks, observability practices, and lineage tracking to ensure trust in data assets
- Collaborate with clients to understand business needs and translate them into data solutions
- Communicate technical designs to both technical teams and business stakeholders
- Support AI/ML systems by building feature engineering pipelines and enabling data access for autonomous agents
What We’re Looking For
- Minimum of 2 years (3+ for Senior Associate) building and deploying data pipelines in production
- Strong proficiency in SQL, Python, and PySpark for large-scale data processing
- Experience with dbt, including modular modeling, testing, and documentation
- Familiarity with cloud data platforms such as Databricks, Snowflake, or Microsoft Fabric
- Knowledge of open table formats and data lakehouse architectures
- Understanding of workflow orchestration tools and dependency management
- Solid grasp of data modeling techniques including dimensional modeling and data vault
- Ability to work with cross-functional teams and communicate technical concepts clearly
- Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience
Preferred Background
- Industry experience in Financial Services, Manufacturing, or Energy & Utilities
- Work with feature stores, vector databases, or RAG/LLM workloads
- Experience in real-time data processing with Kafka, Spark Streaming, or Azure Event Hubs
- Familiarity with data observability tools like Great Expectations, Soda, or Monte Carlo
- Knowledge of DataOps, CI/CD automation, and version-controlled pipeline deployment
- Cloud certifications (e.g., Snowflake, Databricks, Azure, or AWS)
- Consulting experience or client-facing technical roles
- Contributions to open-source data projects or community engagement
Work Environment
This is a hybrid role with flexibility to work remotely and on-site. Periodic travel to client locations may be required. You’ll join a team of engineers and data scientists focused on delivering production systems that drive real business outcomes for enterprise clients.
Why This Matters
You’ll work across industries, solving diverse data challenges and shaping the future of data engineering within a growing technical practice. The systems you build will directly influence critical decisions—from dashboards to machine learning applications—giving you measurable impact. Continuous learning is supported through conference access, certification funding, and collaboration with skilled practitioners who value technical excellence.
