Senior Data Engineer

Job Title:

Required Years of Experience:

Location:

Position Overview

Own the end-to-end data lifecycle from source acquisition and pipelines to analytics-ready models, metric layers, and trustworthy dashboards. Partner with Analysts/DS/Product to turn raw data into business decisions.

Key Responsibilities

1. Integrate sources (DBs, SaaS, APIs, streams) using Fivetran/Airbyte/CDC (Debezium) and custom connectors.
2. Build & operate batch/streaming ETL/ELT on lake/warehouse with Airflow/Prefect + dbt tests, SLAs, lineage, alerting.
3. Design star/snowflake marts and certified datasets; define conformed dimensions and slowly changing dimensions.
4. Implement/maintain semantic layer & metric definitions (LookML/MetricFlow/Transform) for consistent KPIs.
5. Define business KPIs/metrics and build semantic layers/certified datasets for consistent reporting across BI tools or dashboards.
6. Partner on KPI design, cohorting, and A/B test/experimentation data (exposure logs, guardrails, CUPED basics).
7. Build subject-area marts for key domains (e.g., Sales, Product, Marketing, Finance).
8. Drive data discovery (catalog, tags) and ad-hoc analysis support with performant SQL models.
9. Enforce access controls, PII handling, retention; maintain catalog/lineage (Glue/Data Catalog/OpenLineage).

Required Qualifications

1. Advanced SQL (window functions, performance tuning) and Python (PySpark/pandas).
2. Orchestration: Airflow/Prefect; Transformations/Tests: dbt.
3. Warehouses: Snowflake/BigQuery/Redshift/Synapse; Lakes: S3/GCS/ADLS.
4. Streaming: Kafka/Kinesis/Pub/Sub; Connectors/CDC: Fivetran/Airbyte/Debezium.
5. Observability/Quality: Great Expectations/Monte Carlo/Datadog; CI/CD with Git.
6. Hands-on with BI tools (Power BI/Tableau/Looker) and semantic modeling.
7. Experience defining KPIs/metrics, building dashboards, and supporting experimentation pipelines.
8. Ability to translate business questions into data models and SQL; strong storytelling with data.
9. Practical AWS/GCP/Azure (storage, compute, IAM, networking basics).
10. Basic understanding of Generative AI and Agentic AI

Preferred Qualifications

1. Batchelor or Master's in Computer Science, AI, NLP, or related field
2. 6+ year of relevent Experience in Data Engineering