The ideal candidate combines deep hands-on engineering expertise with strong data modeling and solution design skills and can independently lead complex engineering efforts from requirements through production implementation.

Responsibilities

Advanced Data Engineering •    Design, build, and maintain scalable data pipelines and data processing solutions using Databricks, Apache Spark, PySpark, Python, and SQL. •    Develop robust ingestion, transformation, and publishing processes supporting batch and, where appropriate, near-real-time data workloads. •    Engineer reusable frameworks and patterns that improve development consistency, reliability, maintainability, and speed of delivery. •    Design solutions capable of processing large and complex datasets while maintaining appropriate performance and scalability. •    Troubleshoot complex data pipeline, transformation, performance, and integration issues. •    Optimize Databricks workloads for processing efficiency, reliability, and cost. Databricks & Lakehouse Engineering •    Develop enterprise data solutions leveraging Databricks Lakehouse architecture and Delta Lake. •    Design and maintain structured data layers that move data from raw ingestion through validated, curated, and business-consumption layers. •    Implement scalable transformation patterns using Spark SQL, PySpark, SQL, and Delta Lake. •    Apply Databricks engineering capabilities such as Unity Catalog, workflows/jobs, Delta tables, and data governance controls where appropriate. •    Support performance tuning, workload optimization, monitoring, and production support for Databricks-based solutions. •    Partner with platform and architecture teams to establish repeatable Databricks engineering standards and development patterns. Advanced Data Modeling •    Design scalable logical and physical data models supporting enterprise reporting, analytics, and downstream data products. •    Develop dimensional models, fact and dimension structures, curated analytical datasets, and reusable semantic-ready data structures. •    Translate complex operational source data into intuitive business-oriented models. •    Establish appropriate grain, keys, relationships, historical handling, and business rules within analytical data models. •    Balance normalization, dimensional modeling, and denormalized analytical structures based on workload and consumption needs. •    Partner with BI and analytics teams to optimize models for Power BI and other analytical consumers. Data Quality & Governance •    Build automated data validation and reconciliation capabilities into engineering pipelines. •    Establish controls for completeness, accuracy, timeliness, uniqueness, and referential integrity. •    Implement appropriate monitoring, exception handling, logging, and alerting. •    Partner with data governance teams to improve metadata, lineage, documentation, and ownership of enterprise data products. •    Ensure engineering solutions comply with applicable security, privacy, and enterprise data-management standards. Technical Leadership •    Serve as a senior technical resource for complex data engineering and modeling initiatives. •    Lead technical design discussions and recommend appropriate engineering approaches. •    Perform code and design reviews and help establish development standards and engineering best practices. •    Mentor data engineers and provide guidance on Databricks, Spark, SQL, Python, data modeling, and performance optimization. •    Identify opportunities to simplify legacy processes and modernize existing data solutions. •    Collaborate effectively across onshore/offshore engineering, analytics, architecture, product, and business teams. Required Qualifications •    7+ years of professional experience in data engineering, data warehousing, business intelligence engineering, or related disciplines. •    3+ years of hands-on experience developing production solutions using Databricks and Apache Spark. •    Advanced proficiency in SQL and strong experience with Python/PySpark. •    Strong hands-on experience with Delta Lake and modern lakehouse engineering patterns. •    Demonstrated experience designing and implementing complex ETL/ELT pipelines. •    Strong understanding of dimensional data modeling, including fact tables, dimensions, surrogate keys, slowly changing dimensions, and analytical data structures. •    Experience designing solutions for large-volume enterprise datasets. •    Demonstrated ability to diagnose and optimize complex SQL, Spark, and data-processing workloads. •    Experience implementing data validation, reconciliation, monitoring, and production support processes. •    Experience working with source-to-target mappings and translating business requirements into technical data solutions. •    Strong analytical, troubleshooting, documentation, and communication skills. •    Ability to independently own technical solutions while collaborating within distributed global teams. Preferred Qualifications •    Experience with Databricks Unity Catalog and enterprise data governance. •    Experience with Databricks workflow orchestration and production deployment patterns. •    Experience with cloud data platforms and object storage, particularly Microsoft Azure environments. •    Experience supporting Power BI and enterprise semantic/reporting solutions. •    Familiarity with CI/CD and automated deployment practices for data engineering. •    Experience integrating REST APIs, relational databases, SaaS platforms, file-based sources, and other enterprise data sources. •    Experience modernizing legacy ETL or traditional data warehouse workloads into Databricks/lakehouse architecture. •    Knowledge of data lineage, metadata management, access controls, and data governance practices. •    Databricks certifications are a plus. Leadership & Behavioral Competencies The successful candidate will demonstrate: •    Technical ownership: Takes responsibility for solutions from design through production support. •    Engineering judgment: Selects pragmatic solutions based on scalability, reliability, maintainability, performance, and cost. •    Problem solving: Can independently investigate complex data and platform issues and drive them to resolution. •    Quality mindset: Treats data quality, reconciliation, observability, and documentation as core engineering requirements. •    Collaboration: Works effectively across business, analytics, architecture, BI, and engineering teams. •    Technical leadership: Raises the capabilities of the broader engineering team through mentoring, standards, reusable patterns, and design guidance. •    Business orientation: Understands that successful data engineering is measured by the usability and reliability of the resulting data products, not simply by pipeline completion. What Success Looks Like A successful Senior Data Engineer will: •    Deliver reliable, scalable, production-ready Databricks data solutions. •    Reduce complexity by creating reusable engineering patterns rather than one-off pipelines. •    Improve the performance and maintainability of enterprise data workloads. •    Produce well-designed data models that simplify downstream analytics and reporting. •    Improve data trust through automated validation, reconciliation, monitoring, and documentation. •    Provide senior-level technical direction to other engineers while remaining highly hands-on. •    Help advance the Enterprise Intelligence organization toward a more standardized, governed, and scalable lakehouse architecture.

Qualifications

Required Qualifications •    BE with 3-6 years of professional experience in data engineering, data warehousing, business intelligence engineering, or related disciplines. •    3+ years of hands-on experience developing production solutions using Databricks and Apache Spark.