Data Engineer

Required Skills & Experience

• 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.

Nice to Have Skills & Experience

• 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

Job 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.