Mid–Senior Data Analytics Engineer (SQL, Data Modelling, Databricks SQL, DBT)

careers

Mid–Senior Data Analytics Engineer (SQL, Data Modelling, Databricks SQL, DBT)

About the Role

We are looking for a Mid–Senior Data Engineer who will primarily focus on the Gold layer (consumption/use-case layer) of a modern data platform built on Azure and Databricks. While this is a Data Engineering role, it requires a strong data modelling and analytical mindset, as the position bridges the gap between core data engineering and business-facing analytics. You will work closely with business stakeholders and analysts to translate use-case requirements into scalable, production-ready data models using established data platform frameworks.

Key Responsibilities

Design, develop, and maintain end-to-end scalable data pipelines to ingest, process, and transform large-scale datasets across cloud and on-premise environments.
Build and optimize data lake / big data solutions using Azure Data Lake Storage Gen2, Azure Databricks, Azure Data Factory, Apache Spark, and related Azure services.
Develop high-performance Spark applications and optimize distributed data processing workloads.
Leverage programming expertise (primarily Python, with optional Scala/Java) for ETL/ELT pipelines, data transformations, and automation tasks.
Implement streaming data pipelines leveraging tools such as Kafka, Spark Structured Streaming, Flink, or Azure EventHub.
Work with NoSQL databases (Cosmos DB, Cassandra, MongoDB, etc.) and SQL-based data warehouses to enable analytics and reporting use cases.
Ensure performance optimization, cost efficiency, and scalability of big data workloads.
Apply DevOps and Infrastructure-as-Code (IaC) best practices for automated deployment and monitoring of data solutions (Azure DevOps, Terraform, Jenkins, GitHub Actions, etc.).
Collaborate with cross-functional teams (data scientists, analysts, product managers) to translate business requirements into scalable technical solutions.
Maintain high standards of data security, governance, and compliance within cloud environments.

Required Qualifications

2–5+ years of experience in data engineering, analytics engineering, or related roles.
Strong hands-on experience with:
- Azure Data Services (ADLS Gen2, Azure Databricks, ADF, Synapse)
- Cloud Data warehouse (Preferably with Databricks SQL or Synapse, Snowflake)
- SQL (advanced querying, optimization, and data modelling)
- Python with solid programming fundamentals (OOP concepts)
Proven experience in data modelling for analytics/reporting use cases (e.g., star schema, dimensional modelling).
Experience working with large-scale, cloud-based data platforms.
Experience working with different domain data like sales, customer data, finance, e-commerce tracking data, etc.
Familiarity with CI/CD, DevOps, and Infrastructure-as-Code (IaC) practices

Nice-to-Have Skills

Experience with DBT (Data Build Tool) for transformation and modelling.
Know-how of Apache Spark (PySpark, Spark SQL).
Exposure to Power BI or similar BI tools for data consumption.
Knowledge of cloud security and governance best practices.
Relevant certifications in Azure or data engineering.
Contributions to open-source projects in the data ecosystem.
Experience working in e-commerce, sales and marketing domain.

About the Company

EnablerMinds is a next-generation boutique company delivering end-to-end Cloud, Data & AI solutions. Our elite team of data engineers, architects, data scientists, AI engineers and industry specialists empowers enterprises to modernize their data ecosystems, accelerate AI adoption, and unlock transformative business value. With proven methodologies, enterprise-ready frameworks, and an agile delivery and staffing model, we deliver high-performance, scalable, and cost-efficient outcomes tailored to the needs of tomorrow’s intelligent enterprise. We welcome applicants of all genders, backgrounds and identities.