Data Engineer

The Data Engineer II is responsible for designing, building, maintaining, and optimizing data pipelines and systems to support analytics, machine learning, reporting, and operational workflows.

You will support the design, development, and delivery of data infrastructure and solutions. This role will play a pivotal part in building and maintaining scalable, reliable, and efficient data pipelines, operational data stores, data warehouses, and data lakes. The Principal Data Engineer will collaborate with architects, scientists, and analysts to ensure that data is governed, accessible, secure, and aligned with business objectives.

Essential Functions

Data Pipeline Development: Build, automate, and optimize ETL/ELT pipelines - from raw data ingestion to structured and clean datasets
Data Modeling: Design and implement data models for databases, data lakes, or warehouses (e.g., star/snowflake schemas, operational data stores)
Infrastructure & Platforms: Work with cloud platforms (AWS, Google Cloud Platform, Azure) and tools like Redshift, BigQuery, Databricks, or Snowflake. Manage compute, storage, and networking needs related to data
Performance Optimization: Tune SQL queries, pipeline jobs, and storage for efficiency and scalability. Improve system throughput, latency, and cost-efficiency
Data Quality & Validation: Implement monitoring, alerting, and testing for data quality, completeness, and accuracy
Collaboration: Work closely with data analysts, data scientists, software engineers, and product owners. Translate business requirements into technical solutions
Documentation & Standards: Document systems, pipelines, schemas, and transformations. Adhere to coding and operational best practices


Qualifications

2+ years of industry experience in data engineering, analytics engineering, or software engineering
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field or equivalent work experience
Demonstrated experience delivering production-ready data pipelines and ETL/ELT workflows
Previous experience working with cloud data platforms and modern data stacks
Excellent communication and interpersonal skills
Languages: SQL, Python, Java, Scala
Databases: PostgreSQL, MySQL, MongoDB, DynamoDB
Data Processing Frameworks: Apache Spark, Apache Beam, Apache Airflow, DBT, AWS Glue, Databricks, Snowflake, Dagster, etc.
Cloud Platforms: AWS (Glue, Redshift, S3), Google Cloud Platform (BigQuery, Dataflow), Azure (Synapse)
DevOps & CI/CD: Terraform, Docker, Kubernetes (sometimes), GitHub Actions
Monitoring Tools: DataDog, Prometheus, Grafana, custom alerting for data pipelines
Data Governance Basics: Understanding of security, PII/PHI data handling, access control, lineage tools like OpenLineage, Collibra, Atlan, or Collate

All qualified applicants will receive consideration for employment without regard to race, color, national origin, age, ancestry, religion, sex, sexual orientation, gender identity, gender expression, marital status, disability, medical condition, genetic information, pregnancy, or military or veteran status. We consider all qualified applicants, including those with criminal histories, in a manner consistent with state and local laws, including the California Fair Chance Act, City of Los Angeles' Fair Chance Initiative for Hiring Ordinance, and Los Angeles County Fair Chance Ordinance. For unincorporated Los Angeles county, to the extent our customers require a background check for certain positions, the Company faces a significant risk to its business operations and business reputation unless a review of criminal history is conducted for those specific job positions.