Principal Data Engineering Architect

Exciting Opening for Principal Data Engineering Architect – Industrial Data Platforms & IIoT

Dear Folks,

We have an exciting opportunity for the above role @ TAO Digital Solutions (

Interested candidates please forward your updated resume to the following email ID ( ) ASAP.

Experience : 12 to 18+ years overall. Candidate should have min 6+ years in AI/ML; 3+ years leading production AI architecture in asset-intensive environments

Pref Industry : Telecommunications; Energy / Oil & Gas; Automotive / Manufacturing; Aerospace / Aviation

Preferred platform / tool exposure:

Core engineering : Python, SQL, PySpark/Scala, Spark, Kafka/Confluent, dbt, Airflow/Dagster, REST/GraphQL APIs, Git, Docker/Kubernetes, Terraform or equivalent IaC.

Lakehouse / analytics : Databricks + Delta Lake, Snowflake, BigQuery, Redshift, Synapse/Fabric; Apache Iceberg/Hudi familiarity is valuable.

Cloud : strong depth in at least one of AWS, Azure or GCP and architectural familiarity with a second. Examples: S3/Glue/EMR/Kinesis/MSK; ADLS/Data Factory/Event Hubs/Fabric; GCS/Dataflow/Pub/Sub/BigQuery.

Industrial / edge : AWS IoT Core/SiteWise/Greengrass/TwinMaker, Azure IoT Hub/IoT Edge/Digital Twins, MQTT brokers, OPC-UA gateways; familiarity with AVEVA PI/OSIsoft historians, SCADA/DCS and MES/MOM.

Governance : Databricks Unity Catalog, Microsoft Purview, AWS Glue/Lake Formation, Collibra/Alation, data-quality/observability tooling such as Great Expectations, Soda, Monte Carlo or equivalent.

Operational applications : IBM Maximo, SAP PM/EAM, ServiceNow; telecom OSS/BSS/NMS; automotive PLM/MES; aerospace MRO/engineering systems are desirable depending on domain.

Experience & qualifications

12+ years in data engineering, data platforms, software/platform engineering or architecture, with 3+ years owning senior architecture/technical leadership responsibilities.

Proven experience with high-volume, high-velocity telemetry, IoT/IIoT, event, log or time-series data—not only traditional BI/warehouse workloads.

Demonstrated architecture and delivery across at least one asset-intensive industry, with working understanding of maintenance/reliability or operational workflows.

Evidence of building platforms that are used in production by data science/AI and operations teams, including clear SLAs, observability, governance and cost controls.

Strong knowledge of security and networking considerations for hybrid edge-to-cloud and OT/IT integrations.

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Electrical/Computer/Industrial Engineering or related discipline; equivalent industry experience acceptable.

Ability to communicate with plant/site/network engineers, reliability teams, data scientists, cybersecurity, enterprise architects and executive stakeholders.