Lead Data Engineer/Associate Data Architect

ROLE SUMMARY
We are hiring a Lead Data Engineer for a strongly Python-first profile, complemented by solid depth across Databricks, data modelling, T-SQL, 
and data architecture. You will design, build, and own production-grade Python codebases and frameworks at the core of our Databricks 
Lakehouse platform, while also setting technical direction on pipeline architecture, data modelling, and platform performance, and mentoring 
a team of engineers.
KEY RESPONSIBILITIES
• Design, build, and own production-grade Python codebases for the data platform — ETL/ELT pipelines, reusable internal packages, 
schema validation, and testing frameworks (pytest, CI/CD).
• Write clean, well-tested, performant Python: apply OOP, concurrency (multiprocessing/asyncio), decorators, and generators to solve real 
pipeline-scale problems.
• Profile and optimize slow Python pipelines end to end — from algorithmic/code-level fixes to migrating heavy workloads onto 
Databricks/Spark when Python alone won't scale.
• Set and enforce Python engineering standards across the team: code reviews, packaging/versioning, structured logging, and exception
handling patterns.
• Design and deliver Bronze/Silver/Gold Medallion pipelines on Databricks — Delta Lake, Structured Streaming/Auto Loader, and Unity 
Catalog governance.
• Own dimensional data modelling decisions — star/snowflake schema design and SCD strategy — for the platform's core data models.
• Write and review advanced T-SQL for platform workloads — window functions, MERGE/upsert logic, and execution-plan-based 
performance tuning.
• Partner with Data Architects and stakeholders on ETL vs. ELT, orchestration, and cost/performance trade-offs across the platform; mentor 
Data Engineers and Senior Data Engineers through design and code reviews.
MUST-HAVE QUALIFICATIONS
8–12 years in data engineering, including at least 2 years in a technical leadership or mentoring capacity. This role carries a strong Python emphasis, 
complemented by hands-on depth in:
Python & Data Engineering (Primary Focus)
• Strong hands-on Python: OOP, generators, decorators, concurrency (multiprocessing/asyncio), exception handling, and unit testing 
(pytest).
• Experience building production ETL/ELT pipelines, schema validation, and reusable internal Python packages/frameworks.
• Proven ability to profile, debug, and optimize Python code for performance at data-pipeline scale.
Databricks & Lakehouse
• Deep, hands-on Databricks experience: Delta Lake (ACID, time travel, OPTIMIZE/ZORDER/VACUUM), Structured Streaming, Auto Loader, 
and Unity Catalog.
• Experience tuning Spark jobs at scale — data skew, cluster sizing/autoscaling, and cost optimization.
Data Modelling & Warehousing
• Strong grounding in dimensional modelling: star/snowflake schema, SCD Types 1/2/3, fact/dimension grain, and conformed dimensions.
• Working knowledge of Data Vault modelling and Kimball vs. Inmon trade-offs.
T-SQL
• Advanced T-SQL: window functions, MERGE/upsert patterns, indexing strategy, and execution-plan-based query tuning.
Data Architecture
• Proven experience designing Medallion (Bronze/Silver/Gold) architectures and evaluating ETL vs. ELT and batch vs. streaming trade-offs.
• Experience with orchestration (Databricks Workflows, Airflow, or ADF) on Azure or AWS.
GOOD TO HAVE
• PCEP / PCAP (Python Institute) or equivalent Python certification.
• Databricks Certified Data Engineer Professional (or equivalent) certification.
• Experience with data governance/compliance (GDPR/HIPAA) and cost-governance (FinOps) practices.
• Prior experience formally leading or mentoring a team of 3+ engineers.
EDUCATION
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
SOFT SKILLS
Strong stakeholder communication; able to explain technical trade-offs to both engineering and business audiences; comfortable owning 
ambiguous, enterprise-scale design decisions end to end.