Senior Data Engineer 🇺🇸

Rearc•New York, NY

182d

About The Position

As a Senior Data Engineer at Rearc, you'll play a pivotal role in establishing and maintaining technical excellence within our data engineering team. Your deep expertise in data architecture, ETL processes, and data modelling will be instrumental in optimizing data workflows for efficiency, scalability, and reliability. You'll collaborate closely with cross-functional teams to design and implement robust data solutions that meet business objectives and adhere to best practices in data management. Building strong partnerships with both technical teams and stakeholders will be essential as you drive data-driven initiatives and ensure their successful implementation.

Requirements

8+ years of professional experience in data engineering across modern cloud architectures and diverse data systems.
Expertise in designing and implementing data warehouses and data lakes across modern cloud environments (e.g., AWS, Azure, or GCP), with experience in technologies such as Redshift, BigQuery, Snowflake, Delta Lake, or Iceberg.
Strong Python experience for data engineering, including libraries like Pandas, PySpark, NumPy, or Dask.
Hands-on experience with Spark and Databricks (highly desirable).
Experience building and orchestrating data pipelines using Airflow, Databricks, DBT, or AWS Glue.
Strong SQL skills and experience with both SQL and NoSQL databases (PostgreSQL, DynamoDB, Redshift, Delta Lake, Iceberg).
Solid understanding of data architecture principles, data modeling, and best practices for scalable data systems.
Experience with cloud provider services (AWS, Azure, or GCP) and comfort using command-line interfaces or SDKs as part of development workflows.
Familiarity with Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, ARM/Bicep, or AWS CDK.
Excellent communication skills, able to explain technical concepts to technical and non-technical stakeholders.
Adaptability and comfort working in dynamic, fast-changing environments.

Responsibilities

Provide strategic data engineering leadership by shaping the vision, roadmap, and technical direction of data initiatives to align with business goals.
Architect and build scalable, reliable data solutions, including complex data pipelines and distributed systems, using modern frameworks and technologies (e.g., Spark, Kafka, Kubernetes, Databricks, DBT).
Drive innovation by evaluating, proposing, and adopting new tools, patterns, and methodologies that improve data quality, performance, and efficiency.
Apply deep technical expertise in ETL/ELT design, data modeling, data warehousing, and workflow optimization to ensure robust, high-quality data systems.
Collaborate across teams—partner with engineering, product, analytics, and customer stakeholders to understand requirements and deliver impactful, scalable solutions.
Mentor and coach junior engineers, fostering growth, knowledge-sharing, and best practices within the data engineering team.
Contribute to thought leadership through knowledge-sharing, writing technical articles, speaking at meetups or conferences, or representing the team in industry conversations.