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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Insight Global - **Location:** Burbank, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Geographic Information Systems, Adaptable Database Systems, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, BigQuery, Cloud Computing, Cloud Database, Cloud Storage, Software Code Optimization, Code Review, Continuous Integration, Data Cleansing, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Dimensional Modeling, Data Flow Control, Graph Database, Python (Programming Language), Machine Learning, Operational Databases, Query Optimization, Application Data, Search Technologies, SQL Databases, Data Streaming, Unstructured Data, Data Processing, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Snowflake, Apache Spark, Vector Embeddings, Agentic-AI, Git, Data Layers, Event Driven Architecture, Pyspark, Kubernetes, Low Latency, Machine Learning Operations, Data Delivery, Terraform, Data Pipelines, Amazon Redshift, Databricks - **Published:** November 14, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3520250317&tx=FR10804LFU&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 6+ years of Data Engineering or closely related software/data platform experience. * Strong hands-on experience with Python and SQL. * 3+ years building production ETL/ELT pipelines in a code-first environment. * Experience with modern cloud data warehouses or lakehouse architectures such as BigQuery, Snowflake, Databricks, Redshift, or Synapse. * Strong understanding of data modeling, including dimensional models, fact/dimension structures, layered architectures, and reusable analytical datasets. * Experience establishing production standards around data quality, testing, monitoring, lineage, and reliability. * Experience with Git-based development, code reviews, testing, and CI/CD. * Demonstrated experience owning technical decisions, leading projects, or mentoring engineers. * Experience with GCP, particularly BigQuery, Cloud Composer, Pub/Sub, Dataflow, and Cloud Storage. * Hands-on experience with dbt. * Spark or PySpark experience. * Experience with orchestration technologies such as Airflow/Cloud Composer, Dagster, Prefect, or similar platforms. * Experience designing batch and streaming architectures. * Terraform or other infrastructure-as-code experience. * Experience with semantic layers, dimensional modeling, or enterprise analytics platforms. * Experience working with large-scale customer or transactional datasets. * Familiarity with geospatial data. * Experience supporting Data Science teams with feature engineering, training datasets, or analytical data products. * Exposure to machine learning, BQML, Vertex AI, or predictive modeling. * Familiarity with LLMs, RAG, semantic search, vector embeddings, knowledge graphs, or AI agents. * Experience in pricing, recommendations, forecasting, customer analytics, or real-estate-related datasets ## Description We are seeking a Senior Data Engineer to design, build, and scale modern data platforms that support analytics, machine learning, AI, and operational use cases across the organization. This role is highly hands-on and will focus on building reliable data pipelines, scalable data models, strong data-quality frameworks, and reusable platform capabilities. The ideal candidate is strong in Python, SQL, data modeling, and cloud-based data engineering, enjoys solving complex data problems, and can help influence engineering standards., What You'll Do Data Engineering & Pipeline Development * Architect, build, and maintain batch and streaming data pipelines using technologies such as BigQuery, dbt, Airflow/Cloud Composer, Pub/Sub, and Dataflow. * Design scalable layered data models, dimensional models, semantic layers, and reusable data products that evolve with business needs. * Build reliable ingestion and transformation frameworks across transactional systems, APIs, application data, web events, and third-party sources. * Work with large-scale structured and unstructured datasets, including customer, transactional, web, call-center, text, image, and event data. * Develop reusable Python and SQL frameworks that improve engineering productivity and standardize data delivery. * Build trusted, analytics-ready and model-ready datasets for downstream Data Science, analytics, AI, and operational applications. Data Quality, Governance & Observability * Establish and enforce best practices around data quality, lineage, observability, testing, documentation, and schema governance. * Develop automated validation and monitoring to identify pipeline failures, schema changes, anomalies, and data-quality issues. * Build reliable retry, recovery, and alerting patterns for production pipelines. * Ensure datasets are trustworthy, traceable, secure, and appropriately governed. * Partner with security and platform teams on access controls, sensitive-data handling, and data-governance standards. Performance & Platform Optimization * Optimize BigQuery workloads for performance and cost through partitioning, clustering, query optimization, and workload-aware data modeling. * Evaluate pipeline performance and identify opportunities to improve scalability, latency, reliability, and cloud spend. * Support both batch and event-driven architectures based on business and technical requirements. * Evaluate emerging GCP and third-party technologies and recommend appropriate solutions based on scalability, maintainability, and cost. * Build shared libraries, templates, and technical standards that accelerate development across the engineering team. Data Science & AI Enablement * Partner closely with Data Scientists to build feature pipelines and model-ready datasets supporting forecasting, pricing, recommendations, customer analytics, and other predictive use cases. * Enable AI and LLM initiatives by creating reliable data foundations for conversational analytics, RAG, semantic search, and enterprise AI applications. * Support data preparation and integration for structured and unstructured AI use cases. * Explore capabilities such as BQML, vector search, graph-based data patterns, and AI-assisted development where they provide clear business value. * MLOps expertise is not required, but experience supporting ML or AI teams is a plus. Cross-Functional Collaboration & Technical Leadership * Partner with Data Scientists, analysts, engineers, and business stakeholders to translate ambiguous requirements into scalable production data solutions. * Collaborate with pricing, digital product, analytics, customer, and business teams to understand data requirements and deliver trusted data products. * Participate in architecture and design reviews and clearly communicate technical decisions and trade-offs. * Mentor engineers and help establish engineering standards across development, testing, deployment, and documentation. * Contribute to a culture centered on ownership, curiosity, thoughtful technical debate, and continuous learning.