Senior Data Engineer (Chinese Mandarin Speaker)

Bitus Labs invites a Senior Data Engineer who speaks Chinese Mandarin to join onsite in Irvine, CA. This role focuses on architecting and scaling an AWS based data lakehouse, crafting production grade pipelines in Java and Python, and steering data quality, governance, and platform decisions while mentoring engineers. The position offers a salary of USD 130,000 per year and a benefits package designed to support long term growth and stability.

Location: Irvine, California (onsite). Language requirement: Chinese Mandarin.

Benefits

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Parental leave
• Retirement plan
• Vision insurance

Responsibilities

• Design and implement scalable medallion pattern data lakehouses (Bronze, Silver, Gold) on AWS S3 using Apache Iceberg.
• Develop and maintain high throughput ETL and ELT pipelines with AWS Glue, EMR (Spark), and Lambda.
• Apply schema evolution, partitioning strategies, and Iceberg table compaction to optimize storage and query performance.
• Produce production quality pipeline code in Java and Python, choosing the language based on performance and maintainability needs.
• Build and operate event driven pipelines using Kinesis Data Streams, Kinesis Firehose, or Apache Kafka (MSK).
• Design exactly-once and at-least-once processing semantics for streaming workloads with Flink or Spark Structured Streaming on EMR.
• Manage infrastructure as code with AWS CDK or Terraform for repeatable deployments.
• Optimize cost and performance across AWS services including S3, Glue, Athena, Redshift Spectrum, EMR, Lambda, Step Functions, and EventBridge.
• Enforce data security best practices: IAM least-privilege, KMS encryption, VPC networking, and Lake Formation access control.
• Build and maintain CI/CD pipelines for data workloads using AWS CodePipeline, GitHub Actions, or equivalent tools.
• Implement data quality frameworks such as Great Expectations or Deequ and integrate validation into orchestration.
• Define and enforce data contracts between producing and consuming systems.
• Contribute to data cataloging and lineage tracking via AWS Glue Data Catalog or Apache Atlas.
• Collaborate with data scientists, ML engineers, and analysts to deliver performant, well documented datasets.
• Mentor mid level and junior engineers through code reviews, design discussions, and pair programming.
• Document architecture decisions (ADRs) and contribute to the internal knowledge base.

Requirements

• 5+ years of professional data engineering experience, including at least 3 years on AWS cloud platforms.
• Proven track record delivering production data pipelines at scale (TB+ datasets, high throughput SLAs).
• Experience with data lakehouse architectures — medallion pattern, open table formats (Iceberg preferred; Delta Lake or Hudi acceptable).
• Java: strong command of Java 8+ for Spark jobs, Iceberg connectors, and performance critical components; familiarity with Maven or Gradle.
• Python: proficient in Python 3 for AWS Glue scripts, orchestration logic, data quality checks, and automation tooling; experience with pandas, PySpark, boto3, and packaging practices.
• Storage & Compute: S3, Glue (jobs, crawlers, Data Catalog), EMR (Spark/Flink), Lambda, EC2.
• Streaming: Kinesis Data Streams, Kinesis Firehose, or MSK.
• Orchestration: Step Functions, MWAA (Managed Airflow), or EventBridge Scheduler.
• Querying: Athena, Redshift, or Redshift Spectrum.
• Security & Governance: IAM, KMS, Lake Formation, Secrets Manager, VPC.
• DevOps: AWS CDK or CloudFormation; CodePipeline or equivalent CI/CD tools.
• Apache Spark (PySpark and or Spark Java API) with distributed transformations and performance tuning.
• Apache Iceberg — table maintenance, time travel, snapshot management, partition evolution.
• SQL — advanced SQL including window functions, CTEs, and query optimization.

Tech stack at a glance

• Languages: Java (8+), Python 3
• Cloud Platform: AWS (S3, Glue, EMR, Kinesis, Athena, Lambda, Step Functions, Lake Formation, CDK)
• Processing: Apache Spark, Apache Flink, Spark Structured Streaming
• Table Format: Iceberg (primary), familiarity with Delta Lake or Hudi
• Streaming: Kinesis, MSK, Kinesis Firehose
• Orchestration: MWAA, Step Functions, Airflow
• IaC & CI/CD: AWS CDK, Terraform, GitHub Actions, CodePipeline
• Related Tools: Maven, Gradle, PySpark, boto3, Apache Atlas, Great Expectations, Deequ
• Additional: dbt, SageMaker Feature Store, MLflow, Druid, ClickHouse, Apache Airflow