Moder
Website:
gomoder.com
Job details:
We are looking for a hands-on Data Analytics Engineer who can bridge data engineering and analytics — building reliable, well-modeled, and well-tested data assets that power business decisions. You will own data models end to end in Snowflake using DBT, write performant SQL, and use Python for automation, ingestion, and data quality tooling. You will partner closely with data engineers, analysts, and business stakeholders to turn raw data into trusted, analytics-ready datasets.
Key Responsibilities
• Design, build, and maintain modular, version-controlled data models in dbt on Snowflake, following layered architecture (staging, intermediate, marts).
• Write performant, production-grade SQL for transformations, aggregations, and analytical datasets handling large volumes of data.
• Develop and maintain Python scripts and utilities for data ingestion, orchestration, validation, and automation.
• Optimize Snowflake workloads — warehouse sizing, clustering, query tuning, cost monitoring, and storage management.
• Implement data quality, testing, and documentation using dbt tests, generic tests, custom tests, and exposures.
• Collaborate with data engineering on ingestion pipelines (Snowpipe, external stages, stored procedures, tasks) and source system onboarding.
• Translate business requirements from analysts and stakeholders into clean, well-documented dimensional and reporting models.
• Maintain CI/CD pipelines for dbt projects, including code reviews, environment promotion, and automated testing.
• Establish best practices around naming conventions, model lineage, version control, and incremental processing strategies.
• Support data governance and compliance requirements, including data lineage, access controls, and auditability.
Required Skills & Experience
SQL (Strong)
• Expert-level SQL — complex joins, window functions, CTEs, recursive queries, and query optimization.
• Strong understanding of data modeling: dimensional modeling, star/snowflake schemas, SCD Type 1/2, and normalized vs. denormalized design.
• Proven ability to debug, profile, and tune long-running queries on large datasets.
Snowflake
• Hands-on experience with Snowflake objects: databases, schemas, warehouses, stages, file formats, streams, tasks, and stored procedures.
• Working knowledge of Snowpipe, external tables, time travel, zero-copy cloning, and resource monitors.
• Experience with role-based access control (RBAC), masking policies, and warehouse cost optimization.
• Familiarity with semi-structured data handling (VARIANT, JSON, Parquet) and FLATTEN operations.
DBT
• Production experience building dbt projects in Snowflake (dbt Core or dbt Cloud).
• Strong grasp of dbt models, sources, seeds, snapshots, macros, hooks, and Jinja templating.
• Experience with dbt tests (generic and custom), exposures, and dbt-generated documentation.
• Familiarity with incremental models, materialization strategies, and model lineage.
Python
• Strong Python for data engineering and automation: pandas, requests, boto3, snowflake-connector-python, and SDK integrations.
• Experience writing modular, testable code with proper logging, error handling, and configuration management.
• Comfortable working with REST APIs, SFTP, cloud storage (S3 / Azure Blob), and credential/secret management.
Tooling & Engineering Practices
• Git-based workflows, pull requests, code reviews, and CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps, or similar).
• Experience with orchestration — dbt Cloud scheduler, Airflow, AWS EventBridge / Lambda, or equivalent.
• Familiarity with at least one cloud platform (AWS, Azure, or GCP), preferably AWS or Azure.
Preferred / Nice to Have
• Experience replacing managed ETL tools (Fivetran, Stitch, Matillion) with custom Snowflake-native or Python-based pipelines.
• Knowledge of data observability and lineage tools (Monte Carlo, Atlan, OpenLineage).
• Exposure to BI and visualization tools (Power BI, Tableau, Looker, Streamlit).
• Background in financial services, mortgage, or other regulated industries with strong compliance needs.
• Experience with Snowflake Streamlit apps, Snowpark, or dynamic tables.
• Understanding of data security: PII handling, masking, encryption, and audit logging.
Qualifications
• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field (or equivalent practical experience).
• 8+ years of professional experience in data engineering, analytics engineering, or a closely related role.
• Demonstrated experience delivering production data pipelines or analytics models on Snowflake.
What You Bring
• Strong analytical and problem-solving mindset — you enjoy turning ambiguous requirements into clean data models.
• Excellent communication skills — comfortable working with both technical engineers and non-technical business stakeholders.
• A bias for documentation, testing, and reproducibility — you treat data models like production software.
• Ownership mentality — you ship, monitor, and iterate on what you build.
What We Offer
• The opportunity to shape a modern data platform built on Snowflake and dbt.
• Greenfield work on custom ingestion frameworks and analytics models.
• A collaborative team that values clean code, code reviews, and engineering best practices.
• Competitive compensation, benefits, and continuous learning support.
Click on Apply to know more.