Jackson Lewis P.C.
Join to apply for the
Sr. Data Engineer
role at
Jackson Lewis P.C.
Jackson Lewis is a law firm that focuses on employment and labor law with over 1,000 attorneys nationwide, developing proactive strategies and business‑oriented solutions to create a high‑functioning, inclusive workforce.
Job Summary Senior Data Engineer to design, build, and operate mission‑critical data pipelines and platforms. Lead the development of Apache Airflow (Astronomer) orchestrations, build dbt models within a Medallion Architecture, and ensure high quality, reliability, and performance in a Snowflake environment. Partner closely with analytics, product, and business stakeholders, taking ownership of production support, monitoring, and rapid remediation.
Key Responsibilities Data Pipeline Architecture & Design
Design scalable, testable ELT pipelines structured around the Medallion Architecture.
Model normalized schemas in dbt (staging/3NF → marts) and standard transformation patterns.
Define data contracts, SLAs/SLOs, and versioned interfaces between layers.
Workflow Orchestration (Airflow / Astronomer)
Author, schedule, and operate complex Airflow DAGs; implement custom operators, sensors, and hooks.
Integrate with Boomi (iPaaS) to trigger pipelines and exchange run states.
Deploy and manage Airflow via Astronomer, including environment promotion, secrets, and runtime upgrades.
Instrument alerting, logging, SLAs, and on‑failure callbacks; drive incident response and root cause analysis.
Data Modeling, Quality & Governance
Build and orchestrate dbt models; enforce naming, sources, snapshots, incremental strategies, and tests.
Implement data quality at multiple layers: dbt tests, Great Expectations, anomaly checks, and audit trails.
Partner with data governance on ownership, lineage, RBAC, PII handling, and compliance.
Performance & Cost Optimization
Tune Snowflake warehouses, clustering, caching, and query optimization.
Benchmark pipelines, identify bottlenecks, and implement parallelism and efficient file strategies.
CI/CD & DevEx
Build GitHub‑based CI/CD for Airflow DAGs and dbt projects (linting, unit/integration tests, environment promotion, artifact versioning).
Champion engineering hygiene: code reviews, testing pyramids, IaC for data, and high‑quality documentation.
Observability & Support
Establish end‑to‑end observability (task metrics, data freshness, row‑level validations, lineage).
Own L1 & production support with clear escalation paths and on‑call participation.
Collaboration & Leadership
Work in Agile ceremonies (backlog grooming, sprint planning, demos, retros).
Mentor peers, drive design reviews, and communicate complex topics to non‑technical audiences.
Required Skills & Experience
8+ years in data engineering / platform roles, 5+ years building and operating production pipelines.
Expert in Apache Airflow, Astronomer deployment/ops, and custom operators/hooks.
Proven Python expertise in Airflow context, plugins, and packaging.
Hands‑on Boomi integration experience (triggers, callbacks, API/webhook).
Deep dbt knowledge: modeling, macros, packages, tests, exposures.
Strong Snowflake skills: warehouse sizing, performance tuning, security/RBAC, tasks/streams, cost governance.
Solid Git/GitHub workflow and CI/CD for data projects.
Track record of observability and data quality at scale.
Excellent communication, documentation, stakeholder partnership; self‑starter and systematic problem solver.
Industry experience in legal, professional services, healthcare, or finance (compliance‑heavy environments).
Preferred Qualifications
Experience with data mesh/domain‑driven data ownership and product thinking.
Familiarity with data cataloging/metadata tools (OpenLineage, DataHub, Collibra, Alation, Monte Carlo). Cloud experience with Snowflake and at least one major cloud.
Knowledge of regulatory frameworks and data security best practices (HIPAA, SOC 2, SOX).
Linux server setup and configuration is a plus.
What Success Looks Like (90–180 Days)
90 days: Stable Astronomer environments, standardized DAG patterns, dbt conventions, baseline DQ tests covering critical tables.
180 days: Boomi‑to‑Airflow orchestration fully operational, Gold‑layer marts powering BI, measurable reductions in failed runs and compute costs.
Why Join Us
Shape the firm’s data platform and pipelines that power data‑driven decision‑making.
Collaborate with a high‑impact team and influence architecture and engineering practices.
Competitive compensation, benefits, and strong growth opportunities within an innovative legal services organization.
A culture that values craftsmanship, reliability, and continuous improvement.
Salary For New York City Metro area, the expected salary range for this position is between $155,000 and $175,000. The actual compensation will be determined based on experience and other factors permitted by law.
Equal Employment Opportunity We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law.
#J-18808-Ljbffr
Sr. Data Engineer
role at
Jackson Lewis P.C.
Jackson Lewis is a law firm that focuses on employment and labor law with over 1,000 attorneys nationwide, developing proactive strategies and business‑oriented solutions to create a high‑functioning, inclusive workforce.
Job Summary Senior Data Engineer to design, build, and operate mission‑critical data pipelines and platforms. Lead the development of Apache Airflow (Astronomer) orchestrations, build dbt models within a Medallion Architecture, and ensure high quality, reliability, and performance in a Snowflake environment. Partner closely with analytics, product, and business stakeholders, taking ownership of production support, monitoring, and rapid remediation.
Key Responsibilities Data Pipeline Architecture & Design
Design scalable, testable ELT pipelines structured around the Medallion Architecture.
Model normalized schemas in dbt (staging/3NF → marts) and standard transformation patterns.
Define data contracts, SLAs/SLOs, and versioned interfaces between layers.
Workflow Orchestration (Airflow / Astronomer)
Author, schedule, and operate complex Airflow DAGs; implement custom operators, sensors, and hooks.
Integrate with Boomi (iPaaS) to trigger pipelines and exchange run states.
Deploy and manage Airflow via Astronomer, including environment promotion, secrets, and runtime upgrades.
Instrument alerting, logging, SLAs, and on‑failure callbacks; drive incident response and root cause analysis.
Data Modeling, Quality & Governance
Build and orchestrate dbt models; enforce naming, sources, snapshots, incremental strategies, and tests.
Implement data quality at multiple layers: dbt tests, Great Expectations, anomaly checks, and audit trails.
Partner with data governance on ownership, lineage, RBAC, PII handling, and compliance.
Performance & Cost Optimization
Tune Snowflake warehouses, clustering, caching, and query optimization.
Benchmark pipelines, identify bottlenecks, and implement parallelism and efficient file strategies.
CI/CD & DevEx
Build GitHub‑based CI/CD for Airflow DAGs and dbt projects (linting, unit/integration tests, environment promotion, artifact versioning).
Champion engineering hygiene: code reviews, testing pyramids, IaC for data, and high‑quality documentation.
Observability & Support
Establish end‑to‑end observability (task metrics, data freshness, row‑level validations, lineage).
Own L1 & production support with clear escalation paths and on‑call participation.
Collaboration & Leadership
Work in Agile ceremonies (backlog grooming, sprint planning, demos, retros).
Mentor peers, drive design reviews, and communicate complex topics to non‑technical audiences.
Required Skills & Experience
8+ years in data engineering / platform roles, 5+ years building and operating production pipelines.
Expert in Apache Airflow, Astronomer deployment/ops, and custom operators/hooks.
Proven Python expertise in Airflow context, plugins, and packaging.
Hands‑on Boomi integration experience (triggers, callbacks, API/webhook).
Deep dbt knowledge: modeling, macros, packages, tests, exposures.
Strong Snowflake skills: warehouse sizing, performance tuning, security/RBAC, tasks/streams, cost governance.
Solid Git/GitHub workflow and CI/CD for data projects.
Track record of observability and data quality at scale.
Excellent communication, documentation, stakeholder partnership; self‑starter and systematic problem solver.
Industry experience in legal, professional services, healthcare, or finance (compliance‑heavy environments).
Preferred Qualifications
Experience with data mesh/domain‑driven data ownership and product thinking.
Familiarity with data cataloging/metadata tools (OpenLineage, DataHub, Collibra, Alation, Monte Carlo). Cloud experience with Snowflake and at least one major cloud.
Knowledge of regulatory frameworks and data security best practices (HIPAA, SOC 2, SOX).
Linux server setup and configuration is a plus.
What Success Looks Like (90–180 Days)
90 days: Stable Astronomer environments, standardized DAG patterns, dbt conventions, baseline DQ tests covering critical tables.
180 days: Boomi‑to‑Airflow orchestration fully operational, Gold‑layer marts powering BI, measurable reductions in failed runs and compute costs.
Why Join Us
Shape the firm’s data platform and pipelines that power data‑driven decision‑making.
Collaborate with a high‑impact team and influence architecture and engineering practices.
Competitive compensation, benefits, and strong growth opportunities within an innovative legal services organization.
A culture that values craftsmanship, reliability, and continuous improvement.
Salary For New York City Metro area, the expected salary range for this position is between $155,000 and $175,000. The actual compensation will be determined based on experience and other factors permitted by law.
Equal Employment Opportunity We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law.
#J-18808-Ljbffr