Sr. Data Engineer

What You’ll Bring to the Team:

Lead the design and delivery of large, cross-functional data engineering initiatives. Build scalable, reliable, secure, and well-governed data solutions that support analytics, reporting, business applications, and emerging AI/ML use cases.

You Will:

• Lead the design and implementation of scalable batch and real-time data pipelines.
• Define and enforce standards for data modeling, reliability, testing, documentation, observability, security, and governance.
• Drive root-cause analysis, performance optimization, and remediation of systemic data and platform issues.
• Partner with product, engineering, analytics, data science, and business leaders on roadmap and prioritization.
• Engineer scalable data pipelines and data models across PostgreSQL, MongoDB, Snowflake, and AWS using modern ETL/ELT and DataOps practices.
• Develop and maintain transformation workflows using dbt, including reusable models, testing, documentation, and lineage.
• Design and support real-time and event-driven data pipelines using Kafka and related streaming technologies.
• Implement data integration and Master Data Management (MDM) practices to improve data consistency, quality, governance, and integrity across enterprise systems.
• Build curated datasets and semantic layers that support analytics and business intelligence platforms such as Amazon QuickSight and Tableau.
• Develop data solutions that support machine learning, AI, and Generative AI use cases, including preparation of structured and unstructured data.
• Support RAG and LLM-based applications through data ingestion, transformation, enrichment, embeddings, vector search, and retrieval pipelines.
• Apply data quality, lineage, security, governance, and access-control practices to data used across analytics and AI/ML applications.
• Leverage AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent to improve engineering productivity, testing, documentation, and code quality.
• Mentor engineers across multiple levels and contribute to technical strategy, architecture reviews, code reviews, and engineering best practices.
  
  

An Ideal Candidate is/has:

Experience

• 5+ years relevant experience
• A bachelor’s or master’s degree in computer science, data science, information science or related field, or equivalent work experience 
• Deep, hands-on expertise in data engineering, cloud data platforms, data integration, and data modeling, with practical experience supporting analytics and AI/ML use cases.

Data Engineering & Databases

• PostgreSQL / SQL: Advanced SQL development, query optimization, indexing, performance tuning, backup, recovery, and security.
• MongoDB: NoSQL data modeling, query optimization, indexing, performance, and administration concepts.
• Snowflake: Data warehousing, performance optimization, scalable data processing, security, and cost optimization.
• dbt: ELT transformation frameworks, modular data models, testing, documentation, lineage, and CI/CD integration.
• ETL/ELT: Design, development, optimization, monitoring, and troubleshooting of enterprise data pipelines.
• Data Modeling: Dimensional, analytical, transactional, and normalized data models.
• Master Data Management (MDM): Data standardization, master/reference data, data quality, governance, and enterprise data integration.
• Data Quality & Governance: Data validation, integrity, lineage, metadata, observability, and governance.
• Workflow & Orchestration: Airflow or equivalent workflow orchestration technologies.
• Streaming: Kafka and event-driven data processing.

AWS & Cloud Data Engineering

Hands-on experience with the AWS ecosystem and cloud-native data engineering, including relevant services such as:

• Amazon S3
• Amazon MSK / Kafka
• AWS Lambda
• Amazon RDS
• Amazon Aurora
• AWS IAM
• Amazon QuickSight

Ability to design secure, scalable, highly available, and cost-effective cloud data solutions using AWS services.

Analytics & Business Intelligence

• Experience preparing and serving trusted, governed datasets for BI and analytics.
• Amazon QuickSight — dashboards, datasets, data preparation, and analytics.
• Tableau — dashboards, reporting, data sources, and analytical data models.
• Strong understanding of the relationship between data engineering, semantic models, BI, and business reporting requirements.

AI/ML & Generative AI — Working Experience

• Practical experience preparing and managing data for machine learning and AI applications.
• Understanding of data preparation, transformation, feature engineering, and training datasets.
• Familiarity with LLM data pipelines and the role of data engineering in supporting LLM-based applications.
• Understanding of AI/ML data governance concepts, including data quality, lineage, privacy, security, and access controls.
• Experience using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent.
• Ability to collaborate effectively with data scientists, ML engineers, and application engineers on AI/ML data requirements.
• Exposure to RAG and LLM-based applications

DevOps, DataOps & Engineering Practices

• DataOps and CI/CD practices for data pipelines and transformations.
• Automated data testing and quality validation.
• Git-based development and code review practices.
• Infrastructure as Code using Terraform.
• Monitoring, observability, logging, alerting, and production support.
• Strong documentation and engineering compliance practices.

Core Strengths

• Communication — Advanced
• Ownership — Advanced
• Accountability — Advanced
• Technical Leadership
• Problem Solving
• Cross-functional Collaboration
• Mentoring & Knowledge Sharing
• Continuous Improvement

Bonus Points if you have:

• AWS, Snowflake, MongoDB, PostgreSQL, or dbt certifications
• Experience with Databricks, Spark, Delta Lake, or other lakehouse technologies
• Experience with advanced Kafka/event-streaming architectures
• Experience with vector databases and vector search
• Exposure to MLOps and machine learning data pipelines
• Experience with AWS AI/ML services such as Amazon Bedrock or SageMaker
• Experience with data observability platforms
• Experience implementing enterprise data governance and MDM solutions
• Experience supporting large-scale enterprise analytics and BI environments