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