Data Engineer II

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.

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.

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.