Data Engineer

Overview
A fast-growing organization is seeking a Senior Data Engineer to design, build, and scale its core data platform. This role owns foundational data infrastructure and helps establish best practices that enable reliable analytics, reporting, and AI-driven use cases across the business.

This position operates as a senior individual contributor with meaningful technical leadership responsibilities. You’ll work closely with analytics, product, operations, and engineering teams to ensure data is accurate, well-modeled, and easy to consume. The role is well suited for someone who enjoys hands-on engineering while also influencing architecture, standards, and long-term platform direction.

Key Responsibilities

• Design, build, and maintain scalable and reliable data pipelines using modern orchestration frameworks
  (e.g., AWS MWAA, managed schedulers on Azure)

• Develop and optimize analytics-ready data models using transformation frameworks
  (e.g., dbt on Snowflake, Redshift, or Databricks)

• Architect and maintain modern data lake or lakehouse storage patterns
  (e.g., Amazon S3 or Azure Data Lake with Apache Iceberg tables)

• Build and support both batch and streaming data processing workflows
  (e.g., Apache Spark, Kafka, or cloud-native messaging services)

• Establish and enforce data quality, testing, and governance standards
  (e.g., dbt tests, metadata catalogs, data contracts)

• Partner with analytics and business stakeholders to translate requirements into scalable data solutions

• Support and evolve CI/CD pipelines for data workflows and infrastructure
  (e.g., GitHub Actions, Terraform, Docker)

• Manage and optimize cloud infrastructure supporting data workloads
  (e.g., IAM, compute, container platforms, object storage, relational databases)

• Contribute to engineering best practices through documentation, code reviews, and architectural discussions

• Evaluate and integrate open-source tools to improve platform scalability, cost efficiency, and developer experience

• Collaborate on AI- and ML-enabling data infrastructure, including feature generation and retrieval patterns
  (e.g., data foundations for RAG-style workloads)

Minimum Qualifications

• 5+ years of professional experience in data engineering or analytics engineering

• Strong proficiency in Python and SQL

• Deep experience building data pipelines on cloud platforms
  (AWS preferred; Azure experience also valued)

• Hands-on experience with workflow orchestration systems
  (Apache Airflow preferred; Prefect, Dagster, or similar acceptable)

• Strong experience with dbt and analytics engineering best practices

• Experience working with distributed data processing frameworks
  (Apache Spark preferred; Flink a plus)

• Experience designing or supporting modern data lake or lakehouse architectures
  (Apache Iceberg preferred)

• Familiarity with streaming or messaging systems
  (Kafka, Pulsar, or cloud-native equivalents)

• Experience with infrastructure-as-code and containerization
  (Terraform, CloudFormation, Docker)

• Solid understanding of CI/CD concepts as applied to data systems

• Strong communication skills with both technical and non-technical stakeholders

Preferred Experience

• Experience in healthcare, regulated, or compliance-heavy data environments

• Exposure to ML/AI data pipelines, feature stores, or vector-based retrieval systems

• Experience supporting self-service analytics at scale

• Prior mentoring or informal technical leadership experience

• Experience working with Salesforce data