Data Engineer

About the role

About ShyftLabs

At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact. We’re growing fast and we’re looking for passionate problem-solvers who are ready to turn big ideas into real outcomes.

The Opportunity

ShyftLabs is seeking a skilled Data Engineer to support in designing, developing, and optimizing big data solutions using the Databricks Unified Analytics Platform. This role requires strong expertise in Apache Spark, SQL, Python, and cloud platforms (AWS/Azure/GCP). The ideal candidate will collaborate with cross-functional teams to drive data-driven insights and ensure scalable, high-performance data architectures.

What You'll Be Doing -

Design, implement, and optimize big data pipelines in Databricks.

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Develop scalable ETL workflows to process large datasets.

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Leverage Apache Spark for distributed data processing and real-time analytics.

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Implement data governance, security policies, and compliance standards.

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Optimize data lakehouse architectures for performance and cost-efficiency.

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Collaborate with data scientists, analysts, and engineers to enable advanced AI/ML workflows.

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Monitor and troubleshoot Databricks clusters, jobs, and performance bottlenecks.

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Automate workflows using CI/CD pipelines and infrastructure-as-code practices.

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Ensure data integrity, quality, and reliability in all pipelines.

What You Bring -

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

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5+ years of hands-on experience with Databricks and Apache Spark.

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Proficiency in SQL, Python, or Scala for data processing and analysis.

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Experience with cloud platforms (AWS, Azure, or GCP) for data engineering.

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Strong knowledge of ETL frameworks, data lakes, and Delta Lake architecture.

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Experience with CI/CD tools and DevOps best practices.

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Familiarity with data security, compliance, and governance best practices.

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Strong problem-solving and analytical skills with an ability to work in a fast-paced environment.

Nice to Have -

Databricks certifications (e.g., Databricks Certified Data Engineer, Spark Developer).

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Hands-on experience with MLflow, Feature Store, or Databricks SQL.

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Exposure to Kubernetes, Docker, and Terraform.

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Experience with streaming data architectures (Kafka, Kinesis, etc.).