Ledgent Tech
Job Description
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, GCP, 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), GCP (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.
Job Reference: JN -092025-404538
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, GCP, 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), GCP (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.
Job Reference: JN -092025-404538