Job Summary
The Azure Data Architecture Lead is responsible for providing architectural leadership in designing, implementing, and optimizing large-scale data solutions using Azure Data Factory and related Azure services. This role drives enterprise-scale data initiatives, ensuring solutions are robust, scalable, and aligned with industry best practices. The position plays a critical part in shaping data strategy, enforcing governance, and fostering innovation to support organizational and client objectives.
Key Responsibilities
Design and implement scalable data architectures using Azure services
Build and optimize ETL/ELT pipelines using Azure Databricks and Apache Spark
Architect data lake and data warehouse solutions using Azure Data Lake and Azure Synapse Analytics
Define data modeling strategies (batch & real-time processing)
Ensure data security, governance, and compliance standards
Collaborate with stakeholders to understand business requirements and translate them into technical solutions
Optimize performance and cost efficiency of data workloads
Implement CI/CD pipelines for data engineering workflows
Mentor and guide data engineers and development teams
Integrate advanced analytics, AI, and ML solutions when required
Skill Requirements
Strong experience with Azure Databricks and Spark (PySpark/Scala)
Hands-on experience with Azure services (ADF, ADLS, Synapse, Event Hub)
Expertise in big data architecture and distributed systems
Strong knowledge of SQL, Python, and data engineering concepts
Experience with data modeling techniques (star schema, dimensional modeling)
Understanding of real-time streaming (Kafka/Event Hub)
Knowledge of DevOps and CI/CD practices
Strong Development Area:
Data Engineering Foundations
Batch vs streaming; lakehouse concepts; medallion (Bronze/Silver/Gold); file formats (Parquet/Delta/CSV/JSON/Avro); partitioning & clustering; schema evolution; data governance basics; DevOps/CI-CD for data.
SQL
Joins, subqueries, CTEs, window functions, set operations; aggregation & rollups; MERGE/UPSERT; analytic functions; performance (indexes, partition pruning, statistics); data validation scenarios (dedupe, top-N, SCD keys).
Apache Spark (Core)
Spark architecture (driver/executors), DAG, stages/tasks; RDD vs DataFrame/Dataset; wide vs narrow transformations; shuffle mechanics; caching/persistence; partitioning; broadcast joins; skew handling; checkpointing; job tuning.
PySpark
DataFrame API, Spark SQL; UDF vs pandas UDF; windowing; incremental loads; structured streaming (triggers, watermarks); handling semi-structured data; optimizing with predicates, pushdown, join strategies; error handling; unit testing (pytest + chispa).
Databricks Platform
Workspace basics; clusters (Single Node/All-Purpose/Jobs), cluster policies; DBR/LTS; notebooks & Repos; Jobs & Workflows; Delta Lake & Delta Live Tables; Unity Catalog (catalog/schema/table, permissions, lineage); MLflow basics; secret scopes; DBFS; REST APIs; Databricks Connect.
Delta Lake / Lakehouse Patterns
ACID transactions; schema enforcement/evolution; time travel; OPTIMIZE/ZORDER; VACUUM; CDC patterns (MERGE INTO, change data feed); streaming vs batch Delta; expectations/constraints; table maintenance strategies.
Orchestration & Scheduling
Databricks Workflows; Azure Data Factory/Synapse pipelines; triggers; parameter passing; fail/retry; alerts; integration with AutoSys/Control-M/Jenkins/GitHub Actions; event-driven patterns.
Python (Core for Data)
Core syntax; typing & data structures; file I/O; logging; virtual environments; packaging; testing (pytest); common data libs (pandas, pyarrow); error handling; performance considerations (vectorization, generators).
Data Quality & Testing
Great Expectations/dbx expectations or custom checks; unit/integration tests; reconciliation (row/amount); anomaly detection; contract testing for schemas; data observability (metrics, SLAs, freshness).
Security & Governance
Unity Catalog permissions, row/column-level security; secrets management (Key Vault/Secret scopes); PII handling; audit logs; token management; compliance basics.
Cost & Performance Optimization
Cluster sizing, autoscaling; DBU awareness; spot/preemptible instances; storage formats; caching; efficient joins; job scheduling; monitoring with metrics & Ganglia/Spark UI; cost tagging and chargeback.
Analytical Skills & Problem Solving
Break down data problems; root-cause incidents; propose alternatives; estimate complexity; communicate clearly with stakeholders.
Stake Holder Management
Interaction with Client Stakeholders, Communication.
Other Requirements
2. Microsoft Certified: Azure Data Engineer Associate (Optional But Valuable)
3. Microsoft Certified: Azure Data Scientist Associate (Optional But Valuable