Data Engineer Jobs

About The Position

Requirements

• Exceptional hands-on expertise in Databricks or Snowflake lakehouse platforms.
• Deep proficiency in PySpark or advanced SQL, plus Python for data engineering and automation.
• Proven experience building Medallion architecture with Delta Lake or Iceberg Tables.
• Real-time and batch streaming experience (Lambda or Kappa) using Databricks DLT or Snowflake Dynamic Tables / Snowpipe Streaming.
• Hands-on with orchestration tools — Airflow, Databricks Lakeflow, or Snowflake Openflow; dbt experience a plus.
• Strong data modeling skills (Dimensional, Data Vault, schema design).
• Performance and cost tuning expertise (clustering, partitioning, Z-ordering, warehouse/cluster sizing, FinOps).
• Governance experience with Unity Catalog (Databricks) or Horizon Catalog (Snowflake) for lineage, access control, and data quality.
• Semantic layer experience using Databricks AI/BI Genie / Unity Catalog Metrics or Snowflake Semantic Views / Cortex Analyst.
• AI/ML enablement experience with Databricks Mosaic AI or Snowflake Cortex.
• CI/CD and DevOps fluency — Git, Databricks Asset Bundles or Snowflake CLI / Schemachange, automated testing.
• Cloud ecosystem expertise — AWS (S3, Glue, Kinesis), Azure, or GCP.
• Excellent communication and technical storytelling ability.
• Comfortable operating across ambiguity and complex stakeholder environments.
• Bachelor’s degree required (Computer Science, Engineering, or related field); advanced degree preferred.
• 3-5 years of professional data engineering experience, with proven track record leading architecture and hands-on build for enterprise-scale data platforms, operating in complex or mission-critical data environments, and influencing multiple teams and platforms without direct authority.

Responsibilities

• Personally design and build reference implementations and production-grade frameworks on Databricks or Snowflake.
• Design lakehouse platforms using Delta Lake or Iceberg Tables with Medallion (Bronze/Silver/Gold) architecture.
• Define and evolve enterprise data standards, patterns, and reusable accelerators.
• Ensure solutions align with data governance, security, scalability, and cost-efficiency standards.
• Evaluate technologies through hands-on benchmarking — not vendor decks.
• Build the first working version of complex pipelines, frameworks, and POCs (ingestion, CDC, streaming, DQ, observability, CI/CD).
• Drive emerging tech (Iceberg, Lakeflow, Openflow, Cortex, Mosaic AI) from POC to production rollout.
• Solve high-complexity performance, cost, and governance challenges at petabyte scale.
• Identify and address systemic technical debt and architectural risks.
• Implement Lambda or Kappa architectures using Databricks Structured Streaming / DLT or Snowflake Dynamic Tables / Snowpipe Streaming.
• Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex.
• Partner with Data Science and Analytics teams to operationalize models and AI workflows.
• Collaborate closely with Product, Architecture, Security, Infrastructure, and Analytics leaders.
• Translate business needs into sound technical direction backed by working prototypes.
• Communicate technical trade-offs, risks, and decisions clearly to technical and non-technical stakeholders.
• Influence roadmaps and platform investments through technical insight and de-risking POCs.
• Mentor Senior and Staff Data Engineers through pair-programming, PR reviews, and design coaching.
• Raise engineering maturity by shipping working examples and codifying patterns.
• Foster a culture of technical excellence, learning, and continuous improvement.

Benefits

• Insurance Plans (Medical, Life)
• Pension/401K/RSP (country specific)
• Competitive Bonus
• Mobility Allowance
• Tuition Reimbursement
• Company Holidays
• Volunteering time
• And More…..