Senior Data Engineer

The Senior Data Engineer will design, build, and maintain the scalable data pipelines, models, and infrastructure that power analytics, business intelligence, and machine‑learning products across the company. Partnering closely with business, product, and analytics teams, you will translate complex requirements into elegant, reliable data solutions and help drive the delivery of innovative data products. This role reports to the Senior Manager, Data Engineering.

Build E2E Azure Databricks-based data solutions.

Design, develop, and maintain scalable ETL and streaming data pipelines on Azure Databricks, leveraging Apache Spark, Delta Lake, and Azure Data Lake Storage (ADLS Gen2) to enable reliable lakehouse architectures and ensure efficient ingestion, transformation, and storage of data Build and optimize data models and schemas for analytics, reporting, and operational data stores

Build and optimize Delta Lake / Lakehouse patterns (Bronze/Silver/Gold), including schema evolution and time travel

Develop high-quality PySpark / Spark SQL transformations, optimize joins, partitioning, caching, and shuffle behavior.

Implement and maintain data quality frameworks, including data validation, monitoring, and alerting mechanisms.

Collaborate closely with data architects, analysts, data scientists, and product teams to align data engineering activities with business goals.

Leverage cloud data platforms (Azure, AWS or GCP) to build and optimize data storage solutions, including data warehouses, data lake houses, and real-time data processing.

Develop automation processes and frameworks for CI/CD supported by version control, linting, automated testing, security scanning, and monitoring

Contribute to the maintenance and improvement of data governance practices, helping to ensure data integrity, accessibility, and compliance with regulations such as GDPR.Provide technical mentorship and guidance to junior team members, promoting best practices in software engineering, data engineering, and agile development.

Troubleshoot and resolve complex Azure Databricks platform data infrastructure and pipeline issues, ensuring minimal downtime and optimal performance.

Education and/or

Experience:

Required:

Bachelor's degree in Computer Science, Engineering, Data Science, or a related fieldA minimum of 5 years of hands-on experience in data engineering, designing and building scalable data pipelines, ETL/ELT processesA minimum of 5 years of hands-on experience designing, building, and operating data solutions

Extensive experience with cloud data platforms in Azure, AWS, or Google Strong proficiency with Python, SQL, and Apache Spark for data processing

Proven experience building reusable, metadata-driven data ingestion frameworks using Python and Scala Hands-on experience with modern data-platform components (object storage, Lakehouse engines, orchestration tools, columnar warehouses, streaming services).Proven experience with data modeling, schema design, and performance tuning of large-scale data systems.

Deep understanding of data engineering best practices: code repositories, CI/CD pipelines, test automation, monitoring, and alerting systems.

Skilled at crafting compelling data narratives through tables, reports, dashboards, and other visualization tools

Strong problem-solving and analytical skills with excellent attention to detail.

Excellent communication skills and experience collaborating with technical and business stakeholders.

Preferred:

Master's degree in Computer Science, Engineering Experience building data pipelines in an Azure Databricks environment

Knowledge of Databricks architecture and core components, including Databricks Lakehouse, Delta Lake, Databricks SQL, Apache Spark clusters, Unity Catalog, Databricks Workflows (Jobs), and Databricks Notebooks Hands-on experience integrating Azure Databricks with Azure Dev Ops, Azure Blob Storage / ADLS Gen2, Azure Key Vault, and Azure Data Factory Familiarity with enterprise data modeling tools such as ERwin Data Modeler, including the ability to interpret and apply logical and physical data models to analytical and lakehouse architectures

Experience migrating to—or…