Senior Data Engineer — Remote, Scalable Data Pipelines Leader

Excella

United States

Remote

USD 138,000 - 185,000

Full time

14 days+
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Benefits offered by this job

Flexible work locations
Medical, dental, and vision benefits
Parental Leave 8 weeks
Vacations: 15 days + 6 holidays + 4浮動年
Annual internet reimbursement
Professional development 3 days/year
HeadSpace + TalkSpace access
TechEleX devices program
Back-up emergency care days

Job summary

Excella is a transformative technology firm helping organizations unlock new possibilities through talented people. Lead Data Engineers at Excella design and build modern data solutions, including data lakes and cleansed data repositories, collaborating with cross-functional teams to ensure data availability and quality.

Senior engineers develop robust pipelines using batch and streaming technologies, working with Architects, Data Scientists, and DevOps to deliver scalable data solutions while

Qualifications

  • 8+ years of professional experience in data engineering or related fields.
  • Proven ability to build robust, scalable data pipelines and production-grade ETL/ELT systems.
  • Strong proficiency in SQL, Python, and orchestration tools like Airflow and dbt.
  • 2+ years of hands-on experience with Databricks, including development, data processing, and pipeline optimization in a cloud-based environment
  • Hands-on experience with big data technologies such as Spark, Kafka, and file formats like Parquet, Delta Lake, and Iceberg.
  • Deep experience with AWS cloud data platforms (e.g., AWS Glue, S3, Redshift, EMR, BigQuery, or Azure equivalents).
  • Solid understanding of data modeling, performance optimization, and designing secure, well-structured data stores.
  • Familiarity with data lake and analytical architecture patterns, including Star Schema, schema-on-read, and data quality frameworks.
  • Experience with CI/CD, Git, infrastructure-as-code (e.g., Terraform), and NoSQL databases.
  • Effective communicator with experience working in Agile environments (Scrum/Kanban) and collaborating across technical and non-technical teams.
  • Strong problem-solving skills, a growth mindset, and a passion for learning new technologies.
  • One or more of the following certifications: AWS Certified Machine Learning - Specialty; Data Science Council of America (DASCA) Certifications; Databricks Certified Machine Learning Associate; Databricks Certified Developer for Apache Spark; Databricks Certified Data Engineer Associate; Databricks Certified Data Engineer Professional; AWS Certified Data Analytics - Specialty; Python or Scala Programming Certification.
  • Ability to hold and maintain a DHS Public Trust (requires US Citizenship)

Responsibilities

  • Develop and manage data processes to ensure availability and usability.
  • Create and automate data pipelines and platforms.
  • Write clean, efficient, and well-documented code to support data engineering solutions.
  • Monitor and ensure data quality through automated testing frameworks.
  • Collaborate with Architects, Product Owners, Data Scientists, and DevOps to design, build, and maintain scalable data solutions.
  • Research data acquisition sources and evaluate suitability.
  • Integrate data management solutions into client environments.
  • Identify and mitigate risks to data while ensuring data recovery plans.
  • Build and maintain data repositories, including data warehouses, data lakes, and operational data stores.

Skills

SQL
Python
Airflow
dbt

Tools

Databricks
Spark
Kafka
Parquet
Delta Lake
Iceberg
AWS Glue
S3
Redshift
EMR
BigQuery
Azure equivalents

Job description

Excella is a transformative technology firm helping organizations unlock new possibilities through talented people. Lead Data Engineers at Excella design and build modern data solutions, including data lakes and cleansed data repositories, collaborating with cross-functional teams to ensure data availability and quality.

Senior engineers develop robust pipelines using batch and streaming technologies, working with Architects, Data Scientists, and DevOps to deliver scalable data solutions while

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