Lead Data Engineer

Duration :
Full Time

Experience:

12–18 Years of Experience

Own the end-to-end lakehouse architecture:
Bronze / Silver / Gold layer contracts, zone layout on ADLS Gen2, Delta Lake table design, partitioning, schema evolution, and retention strategy.

Design and build metadata-driven, parameterized ingestion frameworks that onboard new data sources without bespoke pipeline code for every feed.

Write canonical PySpark and Scala Spark transformation jobs that serve as the team reference; set coding and testing standards, review pull requests, and debug production incidents.

Design for scale and cost: tune Spark clusters and pools, apply partition pruning and caching strategies, and set cost guardrails as data volumes grow.

Build and automate CI/CD for Databricks and ADF pipelines in Azure Dev Ops using Databricks Asset Bundles and Terraform; maintain platform observability with Azure Monitor and Log Analytics.

Design repeatable patterns for batch files, database extracts, CDC feeds, and streaming ingestion using Azure Event Hubs / Kafka and Spark Structured Streaming.

Design and build the AI-augmented metadata ingestion framework that auto-generates bridge documents, DML statements, canonical table definitions, and control metadata from source schemas.

Build AI-assisted source-to-canonical attribute mapping: schema reasoning, data profiling, confidence scoring, and a human review / approval gate before any mapping reaches production.

Generate file-level and record-level validation rules from historical data and metadata analysis, feeding a configurable, rules-engine-backed data quality framework.

Build AI-assisted data quality that analyses patterns across Bronze, Silver, and Gold layers to propose DQ rules beyond predefined checks.

Deliver anomaly detection covering outliers, data drift, schema drift, volume shifts, and reconciliation breaks — with actionable alerting, not noise.

Build AI-assisted automated reconciliation and test-data generation to feed the platform's automated testing framework.

Produce synthetic, privacy-preserving datasets for lower environments using differential privacy, format-preserving masking, and referential-integrity-safe generation.

Design and build an ontology-driven semantic layer and knowledge graph modelling relationships across finance data entities, powering data discovery, semantic integration, and AI/BI tooling.

Build a GPT-powered conversational interface for natural-language querying of financial data: text-to-SQL or semantic-layer-mediated retrieval grounded in the knowledge graph, with row-level and column-level security enforced and every answer traceable to source.

Own end-to-end AI architecture decisions: model selection, RAG and retrieval design, prompt strategy, evaluation harnesses, guardrails, cost and latency budgets, and observability.

Implement Unity Catalog for cataloguing, lineage, and fine-grained access control; define PII classification, masking, tokenization, and encryption standards across every layer.

Take designs through Architecture Review Boards and AI governance forums, covering responsible AI, data residency, model approval, auditability, and human-in-the-loop controls.

Mentor data engineers, run design reviews, and set the engineering patterns the team builds on — without becoming a bottleneck.

Produce documentation and reusable components good enough for the client's team to operate the platform independently at engagement end.

• Expert - level proficiency in Python, Scala, and PySpark, with a strong…