Senior Data engineer

Role & responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines to ingest, transform, and load data

from diverse sources

• Write and optimize advanced SQL queries for data extraction, transformation, validation, and reporting
• Build and maintain data models, warehouses, and semantic layers to support analytics, reporting, and

downstream consumption

• Work with cloud data platforms and services to enhance data processing, storage, and scalability
• Automate and orchestrate end-to-end data workflows using tools such as Airflow, dbt, Cloud

Composer, or Data Factory

• Collaborate with cross-functional stakeholders Data Architects, Data Scientists, Analysts, and

business teams to translate requirements into technical solutions

• Perform data validation, reconciliation, and quality checks to ensure accuracy, consistency, and

governance across pipelines

• Document data pipelines, schemas, and technical processes to support knowledge sharing and

maintainability

• Troubleshoot pipeline failures, performance bottlenecks, and data quality anomalies
• Mentor junior engineers and contribute to best practices, code reviews, and continuous improvement

(scope matched to seniority)

Preferred candidate profile

410 years of relevant experience in Data Engineering, Data Pipeline Development, or a related field

• Advanced SQL — complex joins, CTEs, window functions, and query performance optimization
• Hands-on proficiency in Python and/or PySpark for data transformation, automation, and processing
• Proven experience building and maintaining ETL/ELT pipelines end-to-end
• Hands-on expertise in at least one cloud data platform (AWS, GCP, or Azure) and its associated

warehouse/lake service (Snowflake, BigQuery, Databricks, or Microsoft Fabric)

• Experience with workflow orchestration tools (Airflow, dbt, Cloud Composer, Data Factory, or

similar)

• Strong problem-solving

Good to Have(any one)

• Marketing/Media Data Engineering & MLOps: Data mart and harmonization design across

disparate sources, MLOps and model deployment, model monitoring, cost optimization

• Unstructured Data & GenAI-Adjacent Engineering (GCP): Document AI, Vertex AI, embeddings

and vector search, semantic data modeling, Agentic AI/LangChain exposure

• Cloud-Native Pipeline Engineering (AWS): S3, Lambda, SNS, Step Functions, NumPy/Pandas,

independent end-to-end ownership as an individual contributor

• Databricks DataOps & BI Enablement: Databricks and PySpark at scale, data validation and

reconciliation, BI tool exposure (Power BI, Tableau, or Looker), Git/CI-CD discipline

• Microsoft Fabric Platform Engineering: Data Factory/Dataflows Gen2, Medallion architecture

(Bronze/Silver/Gold, Delta Lake, OneLake), Power BI DAX/Direct Lake, real-time streams

(Eventstreams/KQL), Purview governance

• Team & Delivery Leadership: Mentoring, code reviews, sprint/delivery ownership, cross-functional

and business stakeholder management