Senior Data Engineer

The Role

As a Senior Data Engineer, you will independently lead technical engagements with customers, owning discovery, solution design, demonstrations, and proofs of concept. You will combine strong hands-on data engineering expertise with communication and business acumen to help customers adopt Databricks and achieve measurable outcomes. The role is primarily focused on data engineering, ETL/ELT, data pipelines, data platforms, and Databricks, while also requiring the ability to understand customer requirements, design appropriate solutions, and communicate effectively with technical and business stakeholders.

The Impact You Will Have

• Lead technical discovery and solution design for workloads spanning data engineering, analytics, machine learning, and AI.
• Design and implement scalable data pipelines, ETL/ELT workflows, data integration, and transformation solutions.
• Build and deliver compelling demonstrations and proofs of concept using the Databricks Platform.
• Work hands-on with structured and unstructured data across cloud environments.
• Own relationships with customer engineers, data teams, technical leaders, and executives.
• Translate complex technical capabilities into clear business value and measurable outcomes.
• Develop account-level technical strategies in partnership with the Account Executive.
• Guide architecture decisions and help customers integrate Databricks with cloud, open-source, database, API, and third-party technologies.
• Navigate competitive evaluations and clearly articulate Databricks' technical and business differentiation.
• Create reusable notebooks, data pipelines, solution accelerators, reference architectures, and technical enablement materials.
• Contribute to workshops, seminars, meetups, and the broader technical community.

What We Look For

• 3+ years of hands-on Data Engineering experience, ideally with exposure to solutions architecture, technical pre-sales, consulting, or customer-facing technical roles.
• Strong experience designing, developing, and maintaining ETL/ELT pipelines and data integration workflows.
• Experience with data lakes, data warehouses, lakehouse architectures, and large-scale data processing systems.
• Hands-on experience designing and implementing data solutions on AWS, Azure, or GCP.
• Strong proficiency in Python and SQL. Experience with Scala, Java, or another relevant programming language is a plus.
• Experience with one or more areas such as Apache Spark, ETL/ELT, streaming, data warehousing, data modeling, orchestration, data quality, machine learning, MLOps, or generative AI.
• Experience leading technical discovery, whiteboarding, architecture reviews, and customer presentations.
• Strong presentation, storytelling, and live demonstration skills.
• Ability to communicate effectively with both technical and non-technical audiences.
• Bachelor's or master's degree in computer science, engineering, mathematics, data science, or a related discipline, or equivalent practical experience.

Nice to Have

• Databricks certification or hands-on experience with the Databricks Platform.
• Experience with Unity Catalog, Lakeflow Spark Declarative Pipelines, MLflow, Apache Spark, Delta Lake, Databricks Workflows, or related technologies.
• Experience building production-grade data pipelines and working with high-volume or complex enterprise datasets.
• Background with a data or AI company, cloud provider, or technical consulting firm.
• Experience supporting enterprise or strategic customer accounts.