Data Engineer- GCP/Databricks

AuxoAI Engineering; Bangalore Rural; Hybride; INR 3 200 000 - 5 200 000; Plein temps

Résumé du poste

AuxoAI is seeking a Senior Data Engineer to lead design, development, and optimization of modern data pipelines and cloud-native platforms across batch and streaming workloads in a lakehouse environment. You will mentor junior engineers and collaborate with AI engineers, solution architects, and data scientists.

Based in Bangalore/Mumbai/Hyderabad/Gurgaon (Hybrid — 3 days in office), you will own ingestion, transformation, and curated data delivery, implement governance and security practices,

Qualifications

• 5+ years hands-on data engineering experience building production pipelines.
• Proficient in Databricks on cloud platforms (GCP/AWS/Azure) using Spark, Delta Lake and structured streaming.
• Strong Python and/or Scala programming with solid SQL for data modelling and transformation.
• Experience with data warehousing concepts, ETL/ELT, and large JSON/map/array payloads.
• Familiarity with version control, CI/CD pipelines, and data quality monitoring frameworks.
• Knowledge of Unity Catalog, schema evolution, and data governance practices.

Responsabilités

• Design and build scalable batch and streaming data pipelines across bronze, silver, and gold layers.
• Ingest and transform data using Auto Loader/Spark Structured Streaming/Kafka with cloud storage and orchestration.
• Develop Databricks-based pipelines using Spark and Delta Lake for lakehouse architecture.
• Design and maintain analytical data layers in BigQuery or Databricks SQL with proper partitioning and performance tuning.
• Implement SQL/PySpark transformations for wide and semi-structured data and complex schema changes.
• Collaborate with AI engineers to feed ML models and analytics systems; mentor junior engineers.

Connaissances

Python/Scala

Databricks

Spark

BigQuery

Delta Lake

CI/CD

Git

SQL

Outils

GCP

Databricks

Kafka

Airflow

Terraform

Description du poste

AuxoAI is seeking a Senior Data Engineer to lead the design, development, and optimisation of modern data pipelines and cloud-native platforms. This role is ideal for someone with deep experience building scalable batch and streaming data workflows across cloud and lakehouse environments, strong hands‑on engineering skills, and a drive to mentor junior engineers.

You will work closely with AI engineers, solution architects, and cross-functional teams to build production‑grade pipelines spanning ingestion, transformation, and curated data delivery — enabling high‑quality data for AI and analytics use cases at scale.

Location: Bangalore / Mumbai / Hyderabad / Gurgaon (Hybrid — 3 days in office)

Responsibilities

• Design and build scalable batch and streaming data pipelines across bronze, silver, and gold medallion layers.
• Build historical and incremental ingestion using Auto Loader/Spark Structured Streaming/Kafka feeds, with GCS/Azure/AWS storage and Databricks Jobs orchestration.
• Develop and maintain Databricks-based pipelines using Spark and Delta Lake for lakehouse architecture, including migration of legacy or on‑premises data sources.
• Design and maintain analytical data layers in BigQuery or Databricks SQL, applying best practices in partitioning, clustering, and performance tuning.
• Implement SQL/PySpark transformations for wide and semi‑structured data, including wide‑to‑long processing and typed or hybrid models suited to consumer requirements.
• Collaborate with AI engineers and data scientists to build pipelines that feed ML models, AI agents, and analytical systems.
• Implement data governance, quality controls, and security best practices including schema enforcement, lineage tracking, and access controls.
• Drive engineering best practices across CI/CD, testing, monitoring, and pipeline observability.
• Partner with solution architects to translate data requirements into technical designs.
• Mentor junior data engineers and contribute to documentation, code reviews, and agile ceremonies.

Requirements

• 5+ years of hands‑on experience in data engineering, building and operating production‑grade pipelines.
• Hands‑on experience with Databricks on GCP, including BigQuery, GCS, Databricks, Spark, Delta Lake, and structured streaming.
• Hands‑on experience with Databricks and Apache Spark, including Delta Lake and end‑to‑end lakehouse implementations.
• Strong programming skills in Python and/or Scala, with solid SQL for modelling and transformation.
• Experience with data modelling, ETL/ELT, pipeline orchestration, and data warehousing concepts, including experience working with large, evolving JSON/map/array payloads, wide‑to‑long transformations, event‑time context joins and schema‑change handling.
• Familiarity with Git, CI/CD pipelines, and data quality monitoring frameworks.
• Solid understanding of data architecture, schema design, and performance tuning.
• Experience with Unity Catalog, source reconciliation, schema evolution, correction handling and replay/recovery testing.
• Strong problem‑solving and collaboration skills.

Bonus Skills

• GCP Professional Data Engineer certification.
• Experience with Vertex AI, Cloud Functions, Dataproc, or real‑time streaming architectures.
• Experience with factory or industrial data sources — MES systems, IoT sensor streams, or operational telemetry.
• Familiarity with data governance and cataloguing tools such as Dataplex, Unity Catalog, Atlan, or Collibra.
• Exposure to Docker, Kubernetes, API integration, and infrastructure‑as‑code (Terraform).