Senior Consultant (Databricks Engineer)

Job Description

• The Data Engineer will be the backbone of our data-driven ecosystem, responsible for designing, developing, and maintaining scalable, reliable data pipelines on Databricks and leading cloud platforms.
• You will bridge the gap between raw data sources and actionable insights by integrating diverse data sets, ensuring pristine data quality, and powering analytics, reporting, and machine learning workloads.
• You will work at the intersection of Analytics, Product, and Infrastructure, collaborating with cross-functional teams to elevate our data platform while championing best practices for governance, monitoring, and system reliability.

What You Will Do

Pipeline Engineering & Development

• Develop and maintain robust ETL/ELT pipelines for centralized storage solutions (e.g., Delta Lake)
• Integrate data from a variety of sources: relational databases, REST APIs, log files, streaming platforms, and external vendors.
• Build sophisticated transformation routines to cleanse, normalize, aggregate, and enrich raw datasets.
• Apply advanced data processing techniques to handle complex, nested, or inconsistent data structures.

Architecture & Governance Contribute to internal frameworks and best practices for code development, versioning, and deployment.

• Implement robust data governance policies (access control, lineage, retention) aligned with enterprise standards.
• Partner with infrastructure leaders to advance our cloud-native data platforms (Azure, AWS).
• Explore and pilot new tools and technologies leveraging Azure, Databricks, and related ecosystems.

Analytics & Business Collaboration Partner with Analytics and Product leaders to translate business requirements into operationalized pipelines.

• Attend requirement grooming, refinement, and sprint planning sessions with end-users.
• Develop dashboards, reports, scorecards, and data visualizations to drive business intelligence.
• Perform rigorous SIT, data profiling, and data validation to confirm accuracy and integrity.

Monitoring & Reliability Monitor production pipelines to detect, diagnose, and resolve issues promptly.

• Develop monitoring dashboards, alerting systems, and automated error-handling mechanisms.
• Optimize performance, batch scheduling, and resource utilization across the data stack.
• Validate the completeness and consistency of ETL loads during UAT and production rollouts.

Qualifications & Required skills

• 3+ years of hands-on experience in data engineering, building large-scale, high-performance data pipelines.
• Strong experience designing data solutions, including data modeling, normalization, and distributed computing architectures.
• Extensive hands-on coding with PySpark, Spark SQL, and Databricks Notebooks/Jobs.
• Proficiency in orchestrating pipelines using Azure Data Factory (ADF), Apache Airflow, or similar schedulers.
• Proven experience with both real-time (streaming) and batch processing paradigms.
• Solid experience building pipelines on Azure (with AWS knowledge being a significant plus).
• High-level proficiency in SQL, including window functions, CTEs, and performance tuning.
• Strong understanding of DevOps tools, Git workflows, and CI/CD pipelines.
• Familiarity with Scrum methodology and practical experience working within cross-functional Scrum teams.
• Excellent problem-solving skills and a collaborative mindset.
• Hands-on experience with streaming technologies such as Apache Kafka, Apache Flink, or AWS Kinesis.
• Proven ability to design and implement real-time data processing pipelines.
• Databricks Certified Data Engineer Associate (preferred).
• Databricks Certified Data Engineer Professional (highly preferred).