Senior Data Engineer

TIU Consulting LLC; Glendale (CA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

TIU Consulting LLC in Glendale, CA, is hiring a data engineer for a hybrid role (2-4 days onsite). You will design and maintain large-scale data pipelines using Databricks and Spark, partner with stakeholders to translate requirements into scalable data platform solutions, and govern the platform with Unity Catalog, ACLs, and lineage.

The role requires 5+ years of experience, strong SQL, and proficiency with Airflow, AWS, Docker, and Kubernetes. Collaboration in an agile environment is essential.

Qualifications

• 5+ years of data engineering experience developing and operating large-scale data pipelines.
• Deep hands-on Databricks and Spark (batch and streaming) experience.
• Proficiency with SQL and advanced performance tuning.
• Experience with Airflow (MWAA) for orchestration.
• Familiar with CI/CD and DevOps practices (Git workflows).
• AWS experience; Kubernetes and containerization understanding.

Responsibilities

• Design, write, test, and deploy data pipelines using PySpark, Scala, SQL, Python.
• Translate stakeholder requirements into scalable data platform solutions.
• Govern Databricks platform: Unity Catalog, ACLs, lineage, privacy tooling.
• Explain Spark architecture and pipeline behavior to stakeholders.
• Provide solution architecture across AWS, Databricks, Kubernetes, and Airflow.
• Collaborate with engineers, architects, and product managers in agile/scrum.
• Maintain documentation of platform changes and pipeline configurations.

Skills

Databricks experience

Advanced SQL

Data pipelines and workflows

Education

Bachelor’s Degree in Computer Science or related field

Tools

Databricks

Apache Spark

Airflow (MWAA)

Docker

Kubernetes

Snowflake

Job description

Location: Glendale, CA (Hybrid – 2- 4 Days Onsite)

Duration: Full Time

Mandatory skills –

• Databricks experience (primary requirement)
• Advanced SQL skills
• Experience building and maintaining data pipelines and workflows

Interview Process

• Expected to follow the team's recent hiring process for similar data engineering positions.
• Typically consists of:
  • Technical interview
  • Final interview (potentially onsite)
• Total process generally spans 2-3 interview rounds.

Key Responsibilities:

• Design, write, test, and deploy data pipelines using PySpark, Scala, SQL, Python
• Meet with stakeholders to gather requirements and translate them into scalable data platform solutions
• Understanding of Databricks platform and developer tooling to diagnose errors, audit platform activity, and automate updates across pipelines, objects, and integrations
• Ability to explain Spark architecture and pipeline behavior to stakeholders to diagnose root causes and recommend solutions
• Provide solution architecture across AWS, Databricks, Kubernetes, and Airflow (MWAA), including cross-platform integrations
• Manage Databricks platform governance, including Unity Catalog, ACLs, lineage, and data discovery and privacy tooling
• Build and maintain Kubernetes containers and containerized utilities supporting deployed data platform services
• Apply networking knowledge to troubleshoot connectivity and integration errors across platform components
• Perform platform administration: provision and remove access, assess resource utilization, monitor platform health and cost, and evaluate stakeholder requests
• Collaborate with engineers, architects, and product managers to drive Core Data platform success; participate in agile/scrum ceremonies
• Maintain documentation of platform changes, standards, and pipeline configurations to support data quality and governance.

Qualifications:

• 5+ years of data engineering experience developing and operating large-scale data pipelines
• Deep hands-on experience with Databricks and Apache Spark (batch and streaming), including pipeline development in PySpark and/or Scala
• Strong understanding of Spark architecture—executors, stages, partitioning, shuffle, and performance tuning with ability to explain tradeoffs to technical and non-technical stakeholders
• Proficiency with Databricks platform tooling (API, SDK, CLI) for automation, auditing, governance, and operational troubleshooting
• Proficient in SQL with advanced performance tuning capabilities
• Hands-on production experience with Airflow (MWAA) for orchestrating data pipelines
• Experience managing Databricks platform governance: ACLs, Unity Catalog, lineage, and access provisioning
• Proficiency in Python and at least one additional language (Scala, Kotlin, or SQL-driven pipeline tooling)
• Experience designing and optimizing scalable ETL/ELT pipelines integrating diverse structured and unstructured data sources
• AWS-primary experience (compute, storage, networking, IAM); experience with other cloud providers is transferable
• Proficiency with Docker and Kubernetes for building and maintaining containerized data platform services
• Working knowledge of networking concepts to diagnose cross-platform integration and connectivity issues
• Familiarity with Snowflake and comparable tooling relative to the Databricks ecosystem
• Experience designing and implementing CI/CD and DevOps practices (Git-based workflows)
• Experience implementing data quality checks, monitoring, and logging for pipeline reliability
• Self-starting problem solver with strong analytical and communication skills; willingness to learn new tooling and trends
• Familiar with Scrum and Agile methodologies
• Experience with Snowflake is a plus
• Bachelor’s Degree in Computer Science, Information Systems, or a related field, or equivalent work experience, Master’s Degree is a plus.