Senior Data Engineer

Deep Sync is the industry leader in deterministic identity and AI-powered data solutions. Leveraging our 35-year foundation of compiling direct mail-grade datasets, Deep Sync develops data-first technologies that power marketing, measurement, AI, and business intelligence for agencies and brands with our fully deterministic identity graph, privacy-first audiences, comprehensive data solutions, and integrations with leading platforms and cloud providers.

We are seeking a Senior Data Engineer with deep expertise in data engineering, data architecture, and large-scale data platform design. This role will be responsible for leading the design, development, optimization, and governance of our data infrastructure. The ideal candidate will not only build and maintain complex data pipelines, but also provide technical leadership, mentor team members, and drive best practices in scalability, reliability, and data quality across the organization.

• Design and evolve scalable, secure, and high-performance data architectures supporting large-volume, high-velocity data.
• Establish data engineering standards, design patterns, and best practices.
• Evaluate and recommend tools, frameworks, and technologies for data processing and storage.
• Partner with engineering leadership to align data strategy with business objectives.
• Lead the design, implementation, and maintenance of robust, scalable data pipelines.
• Ensure data quality, validation, lineage, and observability throughout the pipeline lifecycle.
• Implement fault-tolerant and highly available data processing systems.
• Drive automation and CI/CD practices for data workflows.

• Architect and oversee ETL/ELT frameworks transforming raw data into analytical and operational datasets.
• Integrate data from diverse sources including relational databases, APIs, third-party providers, and streaming platforms.
• Collaborate with analytics, product, and engineering teams to deliver reliable data products.

• Monitor, troubleshoot, and optimize pipeline and warehouse performance.
• Identify bottlenecks and implement indexing, partitioning, denormalization, and query optimization strategies.
• Improve cost efficiency and resource utilization of data systems.
• Mentor junior and mid-level data engineers through code reviews, design sessions, and technical guidance.
• Collaborate cross-functionally with product managers, analysts, ML engineers, and software teams.
• Participate in architectural reviews and technical planning initiatives.

• 5–8+ years of experience in data engineering or related fields.
• 5+ years of advanced SQL query design, performance tuning, and optimization.
• 5+ years of experience in data warehouse architecture, implementation, and support.
• 5+ years of hands‑on experience with ETL/ELT design, development, testing, and troubleshooting.
• Proven experience working with large-scale or “big data” environments.

• Expert‑level proficiency in SQL design and implementation.
• Strong experience with Relational Data Warehouse Systems and Data Warehouse Management Systems.
• Deep understanding of indexing, partitioning, denormalization, and query optimization techniques.
• Experience building and optimizing large‑scale data pipelines and distributed architectures.
• Proficiency in one or more programming languages such as Python, Java, or C#.
• Experience with cloud data platforms and modern data tooling (e.g., AWS, Azure, GCP, Spark, Airflow, Snowflake, Databricks, etc.) is highly desirable.
• Strong analytical, troubleshooting, and problem‑solving skills.
• Excellent communication and documentation abilities.
• Experience mentoring engineers and leading technical initiatives.

• Experience with streaming technologies (Kafka, Kinesis, Pub/Sub, etc.).
• Familiarity with data governance, privacy, and compliance frameworks.
• Exposure to machine learning data pipelines or feature engineering workflows.
• Prior experience in marketing data, identity resolution, or large‑scale consumer datasets.

• Kirkland, WA (First 6 months in‑office; hybrid schedule available thereafter—typically remote on…