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Azure Data Architect (IN)
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign scalable data architectures and data lake and warehouse solutions on Azure
Build and optimize ETL/ELT pipelines with Azure Databricks and Apache Spark
Define data modeling strategies for batch and real-time processing
Ensure data security, governance, and compliance standards
Implement CI/CD pipelines for data engineering workflows
From the employer’s posting
Design and implement scalable data architectures using Azure services
Build and optimize ETL/ELT pipelines using Azure Databricks and Apache Spark
Define data modeling strategies (batch & real-time processing)
Ensure data security, governance, and compliance standards
Implement CI/CD pipelines for data engineering workflows
What you’ll bring
All qualificationsCore experience
- Strong experience with Azure Databricks and Spark (PySpark/Scala)
- Hands-on experience with ADF, ADLS, Synapse and Event Hub
- Expertise in big data architecture and distributed systems
- Strong knowledge of SQL, Python, and data engineering concepts
- Data modeling skills (star schema, dimensional modeling)
Qualification wording
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)
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 12, 20261 day ago
- Recorded sightings
- 2
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
HCL Technologies is hiring
357 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- 1% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Scala
- SQL
- Azure
- Databricks
- Delta Lake
- Kafka
- MLflow
- Spark
- PySpark
- Great Expectations
Where the posting names them
- 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)
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).
Required Skills & Qualifications: - Strong experience with Azure Databricks and Spark (PySpark/Scala) - Hands-on experience with Azure services (ADF, ADLS, Synapse, Event Hub)
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.
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.
Job Summary The Azure Data Solutions Architect leads the design and implementation of enterprise-scale data solutions using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. This role provides architectural leadership, ensuring solutions are robust, scalable, and aligned with business strategy and industry best practices. The architect drives innovation, enforces governance, and mentors teams to deliver high-quality, future-ready data platforms that support organizational transformation. Key Responsibilities
Key Responsibilities: - Design and implement scalable data architectures using Azure services - Build and optimize ETL/ELT pipelines using Azure Databricks and Apache Spark
- 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
- 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)
- 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
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).
Other Requirements 1. Microsoft Certified: Azure Solutions Architect Expert (Recommended) 2. Microsoft Certified: Azure Data Engineer Associate (Optional But Valuable
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.
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.
- Experience with data modeling techniques (star schema, dimensional modeling) - Understanding of real-time streaming (Kafka/Event Hub) - Knowledge of DevOps and CI/CD practices
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).
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.
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Has HCL Technologies posted this job before?
Yes. We’ve followed 17 other Azure Data Architect (IN) jobs there since November 2026. All 17 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About HCL Technologies
- People
- About 20,206 in 2024.Benefit plan filings (Form 5500)
- Company
- Listed as HCLTECH.NS.
- H-1B visas
- 754 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
- Layoff notices
- Notices this year and last cover 120 people. The latest was filed March 27, 2026.State WARN notices
Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
Job Summary
The Azure Data Solutions Architect leads the design and implementation of enterprise-scale data solutions using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. This role provides architectural leadership, ensuring solutions are robust, scalable, and aligned with business strategy and industry best practices. The architect drives innovation, enforces governance, and mentors teams to deliver high-quality, future-ready data platforms that support organizational transformation.
Key Responsibilities
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
Required Skills & Qualifications:
- 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
1. Microsoft Certified: Azure Solutions Architect Expert (Recommended)
2. Microsoft Certified: Azure Data Engineer Associate (Optional But Valuable
2. Microsoft Certified: Azure Data Engineer Associate (Optional But Valuable
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