Senior Data Engineer - Data and Analytics
Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Oct 6, 2026. Pattern scores B on the Alion truth index.
What makes this role different
Most data engineering ends at a table. Pattern's ad-tech output leaves the warehouse and spends a client's advertising budget within the hour. A silently wrong join or an unguarded backfill is a customer-facing incident, not a dashboard discrepancy - so correctness, idempotency and data-quality gating are the job, not paperwork after the job.
The system you'll work on
Destiny is Pattern's automated Ads optimizer. Once a day it discovers the keywords worth buying for every eligible product, assembles a wide feature store from performance, bid-history and search-results data, runs 5 machine-learning models, and picks the bid level that hits each product group's return on ad spend (ROAS) and budget target. A second pipeline then pushes those campaign, keyword and budget edits to the marketplace Ads API every 15 minutes.
It is a large, opinionated data system: a roughly 17,000-line orchestrated SQL codebase, a feature and label store several hundred columns wide, 5 model training and batch-scoring jobs, and blocking data-quality gates in front of every outward write. You would be one of the engineers who owns it end to end.
Roles and Responsibilities
Develop, deploy, and support automated, scalable batch data pipelines from a variety of sources into the lakehouse.
Own and extend Airflow orchestration for a multi-DAG, cross-triggered daily pipeline and a 15-minute action pipeline - including branching, parallel task groups, cross-DAG triggers, backfill and full-refresh paths, and safe reruns.
Write and tune large analytical SQL: multi-hundred-column joins, window functions, incremental merges, and the warehouse-sizing and query-profile work needed to keep a daily run inside its window and its budget.
Extend the feature store - add new features and labels, wire them through the join layer, and preserve the leakage and data-completeness conventions that make the models trainable.
Orchestrate model training and batch inference on SageMaker from Airflow: build training and scoring datasets, manage S3 and Parquet round-trips, containerized training images, instance sizing, and loading predictions and metrics back into the warehouse.
Develop and implement data auditing strategies and processes to ensure data quality - including blocking data-quality checks in front of outward writes - and set thresholds that catch bad data without needlessly halting live bidding.
Identify and resolve problems in large-scale data processing workflows; maintain pipeline processes and troubleshoot failures, including on-call triage when a run breaks before market open.
Guard the safety properties of an outward-writing system: idempotency, new-data detection, action validation and invalidation, and audit trails for every change pushed to marketplace.
Collaborate with data scientists, advertising strategists, and platform teams to specify data requirements and provide access to data.
Translate business and analytics requirements - ROAS targets, budget pacing, playbook rules, branded versus non-branded strategy - into a comprehensive data model and pipelines.
Foster data expertise and own data quality for assigned areas of ownership; work with data infrastructure to triage issues and drive to resolution.
Mentor and provide technical direction to other data engineers, and review their SQL and DAG changes.
What "basics of machine learning" means here
Build training and evaluation datasets correctly - train/test splits over time, holdout windows, and a working instinct for target leakage in rolling-window features.
Reason about class imbalance and resampling (many keyword-hours have no clicks), and about clamping or bounding predictions before they drive a bid.
Read regression metrics - MAE, RMSE, MAPE, WMAPE - plus feature importances, and tell “the model got worse” apart from “the upstream data got worse”.
Operate the model lifecycle: retraining cadence, hyperparameters as configuration, prediction and metric persistence, validation tables, and drift monitoring.
Understand how model outputs compose into a decision - here, predicted clicks, conversion rate, cost per click and basket revenue combining into an expected ROAS per bid, net of cannibalization.
You are not expected to invent model architectures - data scientists own the modeling. You are expected to be a competent, unsupervised partner to them, which means being able to:
Required qualifications
Bachelor's degree in Data Science, Data Analytics, Information Management, Computer Science, Information Technology, a related field, or equivalent professional experience.
4+ years of overall professional experience.
4+ years of hands-on experience with SQL and Python, including advanced SQL - window and analytic functions, complex joins, incremental merges, and query tuning.
3+ years building production data pipelines on modern data architectures, with real ownership of scheduling, dependencies, retries and backfills, at scale and across many source systems.
2+ years working with cloud data warehouses such as Snowflake, Redshift or BigQuery.
Production experience with a workflow orchestrator - Airflow strongly preferred - including debugging failed runs in a live system.
Experience orchestrating ML training and batch inference from a scheduler, on SageMaker or an equivalent platform.
Working knowledge of applied machine learning fundamentals as described above: dataset construction, leakage, evaluation metrics, and model lifecycle operations.
Comfort with AWS - at minimum S3 and IAM - and with columnar file formats.
Demonstrated ownership of data quality: testing, monitoring, alerting, and root-cause analysis on pipelines other people depend on.
Excellent software engineering and scripting practice - version control, code review, modular and reviewable changes.
Strong communication skills, in both presentation and comprehension, with the aptitude for cross-collaboration across data management, data science and analytics domains.
Ability to lead and mentor a team of data engineers.
Preferred Qualification
Experience with digital advertising, bidding or auction systems - Amazon Ads, Google Ads, or a demand-side platform.
Advanced Snowflake - streams and tasks, stored procedures, UDFs, clustering, cost and performance tuning.
Experience with time-series data and forecasting, and with hourly or day-parted grains.
Background in big data, non-relational databases, machine learning or data mining.
Experience with data-quality frameworks such as Soda, Great Expectations or dbt tests.
Experience with open-source and distributed data platforms: Spark, Hive, Trino/Presto, Cassandra, DynamoDB or Elasticsearch.
Broader cloud experience: SNS, SQS, SES, Lambda, Glue, ECR and containerized workloads.
Expertise in data governance.
Experience working productively with AI coding agents on a large existing codebase.
Your First 90 days
Days 1-30 - Read the pipeline end to end and shadow a daily run. Ship small SQL and DAG fixes, take your first on-call triage with support, and be able to explain how a bid becomes an edit on marketplace.
Days 31-60 - Own a stage. Add features to the feature store and wire them through, tune a slow task that threatens the run window, and add or re-threshold a data-quality check that catches something real.
Days 61-90 - Lead a change that spans the pipeline and the action layer - a new signal, a new playbook rule, or a reliability improvement - with the tests, monitoring and rollback story that make it safe to leave running.
The company is a rocket ship experiencing phenomenal growth
We have tailwinds and a long runway; we're barely scratching the surface
We have big opportunities that will get you energized and excited
Great benefits including time off, insurance, competitive pay
Why Pattern?
Pattern provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability, genetic information, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.